#### PATTERN | TEXT | PARSER #######################################################################
# -*- coding: utf-8 -*-
# Copyright (c) 2010 University of Antwerp, Belgium
# Author: Tom De Smedt <tom@organisms.be>
# License: BSD (see LICENSE.txt for details).
# http://www.clips.ua.ac.be/pages/pattern

# Source: https://github.com/clips/pattern/pattern/text/__init__.py
# git-commit: 2014-05-10 (2f944cb)

# Modified: 2015-05-02 Markus Killer <m.killer@langui.ch>

####################################################################################################
from __future__ import unicode_literals, absolute_import
import os
import sys
import re
import string
import types
import codecs

from xml.etree import cElementTree
from itertools import chain
from math import log

try:
    MODULE = os.path.dirname(os.path.realpath(__file__))
except:
    MODULE = ""

from _pattern.compat import text_type, string_types, basestring, imap, unicode, binary_type, PY2

from _pattern.text.tree import Tree, Text, Sentence, Slice, Chunk, PNPChunk, Chink, Word, table
from _pattern.text.tree import SLASH, WORD, POS, CHUNK, PNP, REL, ANCHOR, LEMMA, AND, OR

#--- STRING FUNCTIONS ------------------------------------------------------------------------------
# Latin-1 (ISO-8859-1) encoding is identical to Windows-1252 except for the code points 128-159:
# Latin-1 assigns control codes in this range, Windows-1252 has characters, punctuation, symbols
# assigned to these code points.

# String functions
def decode_string(v, encoding="utf-8"):
    """ Returns the given value as a Unicode string (if possible).
    """
    if isinstance(encoding, basestring):
        encoding = ((encoding,),) + (("windows-1252",), ("utf-8", "ignore"))
    if isinstance(v, binary_type):
        for e in encoding:
            try:
                return v.decode(*e)
            except:
                pass
        return v
    return unicode(v)


def encode_string(v, encoding="utf-8"):
    """ Returns the given value as a Python byte string (if possible).
    """
    if isinstance(encoding, basestring):
        encoding = ((encoding,),) + (("windows-1252",), ("utf-8", "ignore"))
    if isinstance(v, unicode):
        for e in encoding:
            try:
                return v.encode(*e)
            except:
                pass
        return v
    return str(v)

decode_utf8 = decode_string
encode_utf8 = encode_string

decode_utf8 = decode_string
encode_utf8 = encode_string

PUNCTUATION = ".,;:!?()[]{}`'\"@#$^&*+-|=~_"

def ngrams(string, n=3, punctuation=PUNCTUATION, continuous=False):
    """ Returns a list of n-grams (tuples of n successive words) from the given string.
        Alternatively, you can supply a Text or Sentence object.
        With continuous=False, n-grams will not run over sentence markers (i.e., .!?).
        Punctuation marks are stripped from words.
    """
    def strip_punctuation(s, punctuation=set(punctuation)):
        return [w for w in s if (isinstance(w, Word) and w.string or w) not in punctuation]
    if n <= 0:
        return []
    if isinstance(string, basestring):
        s = [strip_punctuation(s.split(" ")) for s in tokenize(string)]
    if isinstance(string, Sentence):
        s = [strip_punctuation(string)]
    if isinstance(string, Text):
        s = [strip_punctuation(s) for s in string]
    if continuous:
        s = [sum(s, [])]
    g = []
    for s in s:
        #s = [None] + s + [None]
        g.extend([tuple(s[i:i+n]) for i in range(len(s)-n+1)])
    return g

def deflood(s, n=3):
    """ Returns the string with no more than n repeated characters, e.g.,
        deflood("NIIIICE!!", n=1) => "Nice!"
        deflood("nice.....", n=3) => "nice..."
    """
    if n == 0:
        return s[0:0]
    return re.sub(r"((.)\2{%s,})" % (n-1), lambda m: m.group(1)[0] * n, s)

def pprint(string, token=[WORD, POS, CHUNK, PNP], column=4):
    """ Pretty-prints the output of Parser.parse() as a table with outlined columns.
        Alternatively, you can supply a tree.Text or tree.Sentence object.
    """
    if isinstance(string, basestring):
        print("\n\n".join([table(sentence, fill=column) for sentence in Text(string, token)]))
    if isinstance(string, Text):
        print("\n\n".join([table(sentence, fill=column) for sentence in string]))
    if isinstance(string, Sentence):
        print(table(string, fill=column))

#--- LAZY DICTIONARY -------------------------------------------------------------------------------
# A lazy dictionary is empty until one of its methods is called.
# This way many instances (e.g., lexicons) can be created without using memory until used.

class lazydict(dict):

    def load(self):
        # Must be overridden in a subclass.
        # Must load data with dict.__setitem__(self, k, v) instead of lazydict[k] = v.
        pass

    def _lazy(self, method, *args):
        """ If the dictionary is empty, calls lazydict.load().
            Replaces lazydict.method() with dict.method() and calls it.
        """
        if dict.__len__(self) == 0:
            self.load()
            setattr(self, method, types.MethodType(getattr(dict, method), self))
        return getattr(dict, method)(self, *args)

    def __repr__(self):
        return self._lazy("__repr__")
    def __len__(self):
        return self._lazy("__len__")
    def __iter__(self):
        return self._lazy("__iter__")
    def __contains__(self, *args):
        return self._lazy("__contains__", *args)
    def __getitem__(self, *args):
        return self._lazy("__getitem__", *args)
    def __setitem__(self, *args):
        return self._lazy("__setitem__", *args)
    def setdefault(self, *args):
        return self._lazy("setdefault", *args)
    def get(self, *args, **kwargs):
        return self._lazy("get", *args)
    def items(self):
        return self._lazy("items")
    def keys(self):
        return self._lazy("keys")
    def values(self):
        return self._lazy("values")
    def update(self, *args):
        return self._lazy("update", *args)
    def pop(self, *args):
        return self._lazy("pop", *args)
    def popitem(self, *args):
        return self._lazy("popitem", *args)

class lazylist(list):

    def load(self):
        # Must be overridden in a subclass.
        # Must load data with list.append(self, v) instead of lazylist.append(v).
        pass

    def _lazy(self, method, *args):
        """ If the list is empty, calls lazylist.load().
            Replaces lazylist.method() with list.method() and calls it.
        """
        if list.__len__(self) == 0:
            self.load()
            setattr(self, method, types.MethodType(getattr(list, method), self))
        return getattr(list, method)(self, *args)

    def __repr__(self):
        return self._lazy("__repr__")
    def __len__(self):
        return self._lazy("__len__")
    def __iter__(self):
        return self._lazy("__iter__")
    def __contains__(self, *args):
        return self._lazy("__contains__", *args)
    def insert(self, *args):
        return self._lazy("insert", *args)
    def append(self, *args):
        return self._lazy("append", *args)
    def extend(self, *args):
        return self._lazy("extend", *args)
    def remove(self, *args):
        return self._lazy("remove", *args)
    def pop(self, *args):
        return self._lazy("pop", *args)

#### PARSER ########################################################################################
# Pattern's text parsers are based on Brill's algorithm, or optionally on a trained language model.
# Brill's algorithm automatically acquires a lexicon of known words (aka tag dictionary),
# and a set of rules for tagging unknown words from a training corpus.
# Morphological rules are used to tag unknown words based on word suffixes (e.g., -ly = adverb).
# Contextual rules are used to tag unknown words based on a word's role in the sentence.
# Named entity rules are used to annotate proper nouns (NNP's: Google = NNP-ORG).
# When available, the parser will use a faster and more accurate language model (SLP, SVM, NB, ...).

#--- LEXICON ---------------------------------------------------------------------------------------

def _read(path, encoding="utf-8", comment=";;;"):
    """ Returns an iterator over the lines in the file at the given path,
        strippping comments and decoding each line to Unicode.
    """
    if path:
        if isinstance(path, basestring) and os.path.exists(path):
            # From file path.
            if PY2:
                f = codecs.open(path, 'r', encoding='utf-8')
            else:
                f = open(path, 'r', encoding='utf-8')
        elif isinstance(path, basestring):
            # From string.
            f = path.splitlines()
        else:
            # From file or buffer.
            f = path
        for i, line in enumerate(f):
            line = line.strip(codecs.BOM_UTF8) if i == 0 and isinstance(line, binary_type) else line
            line = line.strip()
            line = decode_utf8(line, encoding)
            if not line or (comment and line.startswith(comment)):
                continue
            yield line
    return

class Lexicon(lazydict):

    def __init__(self, path=""):
        """ A dictionary of known words and their part-of-speech tags.
        """
        self._path = path

    @property
    def path(self):
        return self._path

    def load(self):
        # Arnold NNP x
        dict.update(self, (x.split(" ")[:2] for x in _read(self._path)))

#--- FREQUENCY -------------------------------------------------------------------------------------

class Frequency(lazydict):
    
    def __init__(self, path=""):
        """ A dictionary of words and their relative document frequency.
        """
        self._path = path

    @property
    def path(self):
        return self._path

    def load(self):
        # and 0.4805
        for x in _read(self.path):
            x = x.split()
            dict.__setitem__(self, x[0], float(x[1]))

#--- LANGUAGE MODEL --------------------------------------------------------------------------------
# A language model determines the statistically most probable tag for an unknown word.
# A pattern.vector Classifier such as SLP can be used to produce a language model,
# by generalizing patterns from a treebank (i.e., a corpus of hand-tagged texts). 
# For example:
# "generalizing/VBG from/IN patterns/NNS" and 
# "dancing/VBG with/IN squirrels/NNS"
# both have a pattern -ing/VBG + [?] + NNS => IN.
# Unknown words preceded by -ing and followed by a plural noun will be tagged IN (preposition),
# unless (put simply) a majority of other patterns learned by the classifier disagrees.

class Model(object):
    
    def __init__(self, path="", classifier=None, known=set(), unknown=set()):
        """ A language model using a classifier (e.g., SLP, SVM) trained on morphology and context.
        """
        try:
            from pattern.vector import Classifier
            from pattern.vector import Perceptron
        except ImportError:
            sys.path.insert(0, os.path.join(MODULE, ".."))
            from vector import Classifier
            from vector import Perceptron
        self._path  = path
        # Use a property instead of a subclass, so users can choose their own classifier.
        self._classifier = Classifier.load(path) if path else classifier or Perceptron()
        # Parser.lexicon entries can be ambiguous (e.g., about/IN  is RB 25% of the time).
        # Parser.lexicon entries also in Model.unknown are overruled by the model.
        # Parser.lexicon entries also in Model.known are not learned by the model
        # (only their suffix and context is learned, see Model._v() below).
        self.unknown = unknown | self._classifier._data.get("model_unknown", set())
        self.known = known

    @property
    def path(self):
        return self._path

    @classmethod
    def load(self, lexicon={}, path=""):
        return Model(lexicon, path)

    def save(self, path, final=True):
        self._classifier._data["model_unknown"] = self.unknown
        self._classifier.save(path, final) # final = unlink training data (smaller file).

    def train(self, token, tag, previous=None, next=None):
        """ Trains the model to predict the given tag for the given token,
            in context of the given previous and next (token, tag)-tuples.
        """
        self._classifier.train(self._v(token, previous, next), type=tag)

    def classify(self, token, previous=None, next=None, **kwargs):
        """ Returns the predicted tag for the given token,
            in context of the given previous and next (token, tag)-tuples.
        """
        return self._classifier.classify(self._v(token, previous, next), **kwargs)

    def apply(self, token, previous=(None, None), next=(None, None)):
        """ Returns a (token, tag)-tuple for the given token,
            in context of the given previous and next (token, tag)-tuples.
        """
        return [token[0], self._classifier.classify(self._v(token[0], previous, next))]

    def _v(self, token, previous=None, next=None):
        """ Returns a training vector for the given (word, tag)-tuple and its context.
        """
        def f(v, s1, s2):
            if s2: 
                v[s1 + " " + s2] = 1
        p, n = previous, next
        p = ("", "") if not p else (p[0] or "", p[1] or "")
        n = ("", "") if not n else (n[0] or "", n[1] or "")
        v = {}
        f(v,  "b", "b")         # Bias.
        f(v,  "h", token[0])    # Capitalization.
        f(v,  "w", token[-6:] if token not in self.known or token in self.unknown else "")
        f(v,  "x", token[-3:])  # Word suffix.
        f(v, "-x", p[0][-3:])   # Word suffix left.
        f(v, "+x", n[0][-3:])   # Word suffix right.
        f(v, "-t", p[1])        # Tag left.
        f(v, "-+", p[1] + n[1]) # Tag left + right.
        f(v, "+t", n[1])        # Tag right.
        return v
        
    def _get_description(self):
        return self._classifier.description
    def _set_description(self, s):
        self._classifier.description = s
    
    description = property(_get_description, _set_description)

#--- MORPHOLOGICAL RULES ---------------------------------------------------------------------------
# Brill's algorithm generates lexical (i.e., morphological) rules in the following format:
# NN s fhassuf 1 NNS x => unknown words ending in -s and tagged NN change to NNS.
#     ly hassuf 2 RB x => unknown words ending in -ly change to RB.

class Morphology(lazylist):

    def __init__(self, path="", known={}):
        """ A list of rules based on word morphology (prefix, suffix).
        """
        self.known = known
        self._path = path
        self._cmd  = set((
                "word", # Word is x.
                "char", # Word contains x.
             "haspref", # Word starts with x.
              "hassuf", # Word end with x.
             "addpref", # x + word is in lexicon.
              "addsuf", # Word + x is in lexicon.
          "deletepref", # Word without x at the start is in lexicon.
           "deletesuf", # Word without x at the end is in lexicon.
            "goodleft", # Word preceded by word x.
           "goodright", # Word followed by word x.
        ))
        self._cmd.update([("f" + x) for x in self._cmd])

    @property
    def path(self):
        return self._path

    def load(self):
        # ["NN", "s", "fhassuf", "1", "NNS", "x"]
        list.extend(self, (x.split() for x in _read(self._path)))
        
    def apply(self, token, previous=(None, None), next=(None, None)):
        """ Applies lexical rules to the given token, which is a [word, tag] list.
        """
        w = token[0]
        for r in self:
            if r[1] in self._cmd: # Rule = ly hassuf 2 RB x
                f, x, pos, cmd = bool(0), r[0], r[-2], r[1].lower()
            if r[2] in self._cmd: # Rule = NN s fhassuf 1 NNS x
                f, x, pos, cmd = bool(1), r[1], r[-2], r[2].lower().lstrip("f")
            if f and token[1] != r[0]:
                continue
            if (cmd == "word"       and x == w) \
            or (cmd == "char"       and x in w) \
            or (cmd == "haspref"    and w.startswith(x)) \
            or (cmd == "hassuf"     and w.endswith(x)) \
            or (cmd == "addpref"    and x + w in self.known) \
            or (cmd == "addsuf"     and w + x in self.known) \
            or (cmd == "deletepref" and w.startswith(x) and w[len(x):] in self.known) \
            or (cmd == "deletesuf"  and w.endswith(x) and w[:-len(x)] in self.known) \
            or (cmd == "goodleft"   and x == next[0]) \
            or (cmd == "goodright"  and x == previous[0]):
                token[1] = pos
        return token

    def insert(self, i, tag, affix, cmd="hassuf", tagged=None):
        """ Inserts a new rule that assigns the given tag to words with the given affix,
            e.g., Morphology.append("RB", "-ly").
        """
        if affix.startswith("-") and affix.endswith("-"):
            affix, cmd = affix[+1:-1], "char"
        if affix.startswith("-"):
            affix, cmd = affix[+1:-0], "hassuf"
        if affix.endswith("-"):
            affix, cmd = affix[+0:-1], "haspref"
        if tagged:
            r = [tagged, affix, "f"+cmd.lstrip("f"), tag, "x"]
        else:
            r = [affix, cmd.lstrip("f"), tag, "x"]
        lazylist.insert(self, i, r)

    def append(self, *args, **kwargs):
        self.insert(len(self)-1, *args, **kwargs)

    def extend(self, rules=[]):
        for r in rules:
            self.append(*r)

#--- CONTEXT RULES ---------------------------------------------------------------------------------
# Brill's algorithm generates contextual rules in the following format:
# VBD VB PREVTAG TO => unknown word tagged VBD changes to VB if preceded by a word tagged TO.

class Context(lazylist):

    def __init__(self, path=""):
        """ A list of rules based on context (preceding and following words).
        """
        self._path = path
        self._cmd = set((
               "prevtag", # Preceding word is tagged x.
               "nexttag", # Following word is tagged x.
              "prev2tag", # Word 2 before is tagged x.
              "next2tag", # Word 2 after is tagged x.
           "prev1or2tag", # One of 2 preceding words is tagged x.
           "next1or2tag", # One of 2 following words is tagged x.
        "prev1or2or3tag", # One of 3 preceding words is tagged x.
        "next1or2or3tag", # One of 3 following words is tagged x.
           "surroundtag", # Preceding word is tagged x and following word is tagged y.
                 "curwd", # Current word is x.
                "prevwd", # Preceding word is x.
                "nextwd", # Following word is x.
            "prev1or2wd", # One of 2 preceding words is x.
            "next1or2wd", # One of 2 following words is x.
         "next1or2or3wd", # One of 3 preceding words is x.
         "prev1or2or3wd", # One of 3 following words is x.
             "prevwdtag", # Preceding word is x and tagged y.
             "nextwdtag", # Following word is x and tagged y.
             "wdprevtag", # Current word is y and preceding word is tagged x.
             "wdnexttag", # Current word is x and following word is tagged y.
             "wdand2aft", # Current word is x and word 2 after is y.
          "wdand2tagbfr", # Current word is y and word 2 before is tagged x.
          "wdand2tagaft", # Current word is x and word 2 after is tagged y.
               "lbigram", # Current word is y and word before is x.
               "rbigram", # Current word is x and word after is y.
            "prevbigram", # Preceding word is tagged x and word before is tagged y.
            "nextbigram", # Following word is tagged x and word after is tagged y.
        ))

    @property
    def path(self):
        return self._path

    def load(self):
        # ["VBD", "VB", "PREVTAG", "TO"]
        list.extend(self, (x.split() for x in _read(self._path)))

    def apply(self, tokens):
        """ Applies contextual rules to the given list of tokens,
            where each token is a [word, tag] list.
        """
        o = [("STAART", "STAART")] * 3 # Empty delimiters for look ahead/back.
        t = o + tokens + o
        for i, token in enumerate(t):
            for r in self:
                if token[1] == "STAART":
                    continue
                if token[1] != r[0] and r[0] != "*":
                    continue
                cmd, x, y = r[2], r[3], r[4] if len(r) > 4 else ""
                cmd = cmd.lower()
                if (cmd == "prevtag"        and x ==  t[i-1][1]) \
                or (cmd == "nexttag"        and x ==  t[i+1][1]) \
                or (cmd == "prev2tag"       and x ==  t[i-2][1]) \
                or (cmd == "next2tag"       and x ==  t[i+2][1]) \
                or (cmd == "prev1or2tag"    and x in (t[i-1][1], t[i-2][1])) \
                or (cmd == "next1or2tag"    and x in (t[i+1][1], t[i+2][1])) \
                or (cmd == "prev1or2or3tag" and x in (t[i-1][1], t[i-2][1], t[i-3][1])) \
                or (cmd == "next1or2or3tag" and x in (t[i+1][1], t[i+2][1], t[i+3][1])) \
                or (cmd == "surroundtag"    and x ==  t[i-1][1] and y == t[i+1][1]) \
                or (cmd == "curwd"          and x ==  t[i+0][0]) \
                or (cmd == "prevwd"         and x ==  t[i-1][0]) \
                or (cmd == "nextwd"         and x ==  t[i+1][0]) \
                or (cmd == "prev1or2wd"     and x in (t[i-1][0], t[i-2][0])) \
                or (cmd == "next1or2wd"     and x in (t[i+1][0], t[i+2][0])) \
                or (cmd == "prevwdtag"      and x ==  t[i-1][0] and y == t[i-1][1]) \
                or (cmd == "nextwdtag"      and x ==  t[i+1][0] and y == t[i+1][1]) \
                or (cmd == "wdprevtag"      and x ==  t[i-1][1] and y == t[i+0][0]) \
                or (cmd == "wdnexttag"      and x ==  t[i+0][0] and y == t[i+1][1]) \
                or (cmd == "wdand2aft"      and x ==  t[i+0][0] and y == t[i+2][0]) \
                or (cmd == "wdand2tagbfr"   and x ==  t[i-2][1] and y == t[i+0][0]) \
                or (cmd == "wdand2tagaft"   and x ==  t[i+0][0] and y == t[i+2][1]) \
                or (cmd == "lbigram"        and x ==  t[i-1][0] and y == t[i+0][0]) \
                or (cmd == "rbigram"        and x ==  t[i+0][0] and y == t[i+1][0]) \
                or (cmd == "prevbigram"     and x ==  t[i-2][1] and y == t[i-1][1]) \
                or (cmd == "nextbigram"     and x ==  t[i+1][1] and y == t[i+2][1]):
                    t[i] = [t[i][0], r[1]]
        return t[len(o):-len(o)]

    def insert(self, i, tag1, tag2, cmd="prevtag", x=None, y=None):
        """ Inserts a new rule that updates words with tag1 to tag2,
            given constraints x and y, e.g., Context.append("TO < NN", "VB")
        """
        if " < " in tag1 and not x and not y:
            tag1, x = tag1.split(" < "); cmd="prevtag"
        if " > " in tag1 and not x and not y:
            x, tag1 = tag1.split(" > "); cmd="nexttag"
        lazylist.insert(self, i, [tag1, tag2, cmd, x or "", y or ""])

    def append(self, *args, **kwargs):
        self.insert(len(self)-1, *args, **kwargs)

    def extend(self, rules=[]):
        for r in rules:
            self.append(*r)

#--- NAMED ENTITY RECOGNIZER -----------------------------------------------------------------------

RE_ENTITY1 = re.compile(r"^http://")                            # http://www.domain.com/path
RE_ENTITY2 = re.compile(r"^www\..*?\.[com|org|net|edu|de|uk]$") # www.domain.com
RE_ENTITY3 = re.compile(r"^[\w\-\.\+]+@(\w[\w\-]+\.)+[\w\-]+$") # name@domain.com

class Entities(lazydict):

    def __init__(self, path="", tag="NNP"):
        """ A dictionary of named entities and their labels.
            For domain names and e-mail adresses, regular expressions are used.
        """
        self.tag   = tag
        self._path = path
        self._cmd  = ((
            "pers", # Persons: George/NNP-PERS
             "loc", # Locations: Washington/NNP-LOC
             "org", # Organizations: Google/NNP-ORG
        ))

    @property
    def path(self):
        return self._path

    def load(self):
        # ["Alexander", "the", "Great", "PERS"]
        # {"alexander": [["alexander", "the", "great", "pers"], ...]}
        for x in _read(self.path):
            x = [x.lower() for x in x.split()]
            dict.setdefault(self, x[0], []).append(x)

    def apply(self, tokens):
        """ Applies the named entity recognizer to the given list of tokens,
            where each token is a [word, tag] list.
        """
        # Note: we could also scan for patterns, e.g.,
        # "my|his|her name is|was *" => NNP-PERS.
        i = 0
        while i < len(tokens):
            w = tokens[i][0].lower()
            if RE_ENTITY1.match(w) \
            or RE_ENTITY2.match(w) \
            or RE_ENTITY3.match(w):
                tokens[i][1] = self.tag
            if w in self:
                for e in self[w]:
                    # Look ahead to see if successive words match the named entity.
                    e, tag = (e[:-1], "-"+e[-1].upper()) if e[-1] in self._cmd else (e, "")
                    b = True
                    for j, e in enumerate(e):
                        if i + j >= len(tokens) or tokens[i+j][0].lower() != e:
                            b = False; break
                    if b:
                        for token in tokens[i:i+j+1]:
                            token[1] = token[1] if token[1].startswith(self.tag) else self.tag
                            token[1] += tag
                        i += j
                        break
            i += 1
        return tokens

    def append(self, entity, name="pers"):
        """ Appends a named entity to the lexicon,
            e.g., Entities.append("Hooloovoo", "PERS")
        """
        e = map(lambda s: s.lower(), entity.split(" ") + [name])
        self.setdefault(e[0], []).append(e)

    def extend(self, entities):
        for entity, name in entities:
            self.append(entity, name)

#### PARSER ########################################################################################

#--- PARSER ----------------------------------------------------------------------------------------
# A shallow parser can be used to retrieve syntactic-semantic information from text
# in an efficient way (usually at the expense of deeper configurational syntactic information).
# The shallow parser in Pattern is meant to handle the following tasks:
# 1)  Tokenization: split punctuation marks from words and find sentence periods.
# 2)       Tagging: find the part-of-speech tag of each word (noun, verb, ...) in a sentence.
# 3)      Chunking: find words that belong together in a phrase.
# 4) Role labeling: find the subject and object of the sentence.
# 5) Lemmatization: find the base form of each word ("was" => "is").

#    WORD     TAG     CHUNK      PNP        ROLE        LEMMA
#------------------------------------------------------------------
#     The      DT      B-NP        O        NP-SBJ-1      the
#   black      JJ      I-NP        O        NP-SBJ-1      black
#     cat      NN      I-NP        O        NP-SBJ-1      cat
#     sat      VB      B-VP        O        VP-1          sit
#      on      IN      B-PP      B-PNP      PP-LOC        on
#     the      DT      B-NP      I-PNP      NP-OBJ-1      the
#     mat      NN      I-NP      I-PNP      NP-OBJ-1      mat
#       .      .        O          O          O           .

# The example demonstrates what information can be retrieved:
#
# - the period is split from "mat." = the end of the sentence,
# - the words are annotated: NN (noun), VB (verb), JJ (adjective), DT (determiner), ...
# - the phrases are annotated: NP (noun phrase), VP (verb phrase), PNP (preposition), ...
# - the phrases are labeled: SBJ (subject), OBJ (object), LOC (location), ...
# - the phrase start is marked: B (begin), I (inside), O (outside),
# - the past tense "sat" is lemmatized => "sit".

# By default, the English parser uses the Penn Treebank II tagset:
# http://www.clips.ua.ac.be/pages/penn-treebank-tagset
PTB = PENN = "penn"

class Parser(object):

    def __init__(self, lexicon={}, frequency={}, model=None, morphology=None, context=None, entities=None, default=("NN", "NNP", "CD"), language=None):        
        """ A simple shallow parser using a Brill-based part-of-speech tagger.
            The given lexicon is a dictionary of known words and their part-of-speech tag.
            The given default tags are used for unknown words.
            Unknown words that start with a capital letter are tagged NNP (except for German).
            Unknown words that contain only digits and punctuation are tagged CD.
            Optionally, morphological and contextual rules (or a language model) can be used 
            to improve the tags of unknown words.
            The given language can be used to discern between
            Germanic and Romance languages for phrase chunking.
        """
        self.lexicon    = lexicon or {}
        self.frequency  = frequency or {}
        self.model      = model
        self.morphology = morphology
        self.context    = context
        self.entities   = entities
        self.default    = default
        self.language   = language
        # Load data.
        f = lambda s: isinstance(s, basestring) or hasattr(s, "read")
        if f(lexicon):
            # Known words.
            self.lexicon = Lexicon(path=lexicon)
        if f(frequency):
            # Word frequency.
            self.frequency= Frequency(path=frequency)
        if f(morphology):
            # Unknown word rules based on word suffix.
            self.morphology = Morphology(path=morphology, known=self.lexicon)
        if f(context):
            # Unknown word rules based on word context.
            self.context = Context(path=context)
        if f(entities):
            # Named entities.
            self.entities = Entities(path=entities, tag=default[1])
        if f(model):
            # Word part-of-speech classifier.
            try: 
                self.model = Model(path=model)
            except ImportError: # pattern.vector
                pass
    
    def find_keywords(self, string, **kwargs):
        """ Returns a sorted list of keywords in the given string.
        """
        return find_keywords(string,
                     parser = self,
                        top = kwargs.pop("top", 10),
                  frequency = kwargs.pop("frequency", {}), **kwargs
        )
    
    def find_tokens(self, string, **kwargs):
        """ Returns a list of sentences from the given string.
            Punctuation marks are separated from each word by a space.
        """
        # "The cat purs." => ["The cat purs ."]
        return find_tokens(string,
                punctuation = kwargs.get(  "punctuation", PUNCTUATION),
              abbreviations = kwargs.get("abbreviations", ABBREVIATIONS),
                    replace = kwargs.get(      "replace", replacements),
                  linebreak = r"\n{2,}")

    def find_tags(self, tokens, **kwargs):
        """ Annotates the given list of tokens with part-of-speech tags.
            Returns a list of tokens, where each token is now a [word, tag]-list.
        """
        # ["The", "cat", "purs"] => [["The", "DT"], ["cat", "NN"], ["purs", "VB"]]
        return find_tags(tokens,
                    lexicon = kwargs.get(   "lexicon", self.lexicon or {}),
                      model = kwargs.get(     "model", self.model),
                 morphology = kwargs.get("morphology", self.morphology),
                    context = kwargs.get(   "context", self.context),
                   entities = kwargs.get(  "entities", self.entities),
                   language = kwargs.get(  "language", self.language),
                    default = kwargs.get(   "default", self.default),
                        map = kwargs.get(       "map", None))

    def find_chunks(self, tokens, **kwargs):
        """ Annotates the given list of tokens with chunk tags.
            Several tags can be added, for example chunk + preposition tags.
        """
        # [["The", "DT"], ["cat", "NN"], ["purs", "VB"]] =>
        # [["The", "DT", "B-NP"], ["cat", "NN", "I-NP"], ["purs", "VB", "B-VP"]]
        return find_prepositions(
               find_chunks(tokens,
                   language = kwargs.get("language", self.language)))

    def find_prepositions(self, tokens, **kwargs):
        """ Annotates the given list of tokens with prepositional noun phrase tags.
        """
        return find_prepositions(tokens) # See also Parser.find_chunks().

    def find_labels(self, tokens, **kwargs):
        """ Annotates the given list of tokens with verb/predicate tags.
        """
        return find_relations(tokens)

    def find_lemmata(self, tokens, **kwargs):
        """ Annotates the given list of tokens with word lemmata.
        """
        return [token + [token[0].lower()] for token in tokens]

    def parse(self, s, tokenize=True, tags=True, chunks=True, relations=False, lemmata=False, encoding="utf-8", **kwargs):
        """ Takes a string (sentences) and returns a tagged Unicode string (TaggedString).
            Sentences in the output are separated by newlines.
            With tokenize=True, punctuation is split from words and sentences are separated by \n.
            With tags=True, part-of-speech tags are parsed (NN, VB, IN, ...).
            With chunks=True, phrase chunk tags are parsed (NP, VP, PP, PNP, ...).
            With relations=True, semantic role labels are parsed (SBJ, OBJ).
            With lemmata=True, word lemmata are parsed.
            Optional parameters are passed to
            the tokenizer, tagger, chunker, labeler and lemmatizer.
        """
        # Tokenizer.
        if tokenize is True:
            s = self.find_tokens(s, **kwargs)
        if isinstance(s, (list, tuple)):
            s = [isinstance(s, basestring) and s.split(" ") or s for s in s]
        if isinstance(s, basestring):
            s = [s.split(" ") for s in s.split("\n")]
        # Unicode.
        for i in range(len(s)):
            for j in range(len(s[i])):
                if isinstance(s[i][j], str):
                    s[i][j] = decode_string(s[i][j], encoding)
            # Tagger (required by chunker, labeler & lemmatizer).
            if tags or chunks or relations or lemmata:
                s[i] = self.find_tags(s[i], **kwargs)
            else:
                s[i] = [[w] for w in s[i]]
            # Chunker.
            if chunks or relations:
                s[i] = self.find_chunks(s[i], **kwargs)
            # Labeler.
            if relations:
                s[i] = self.find_labels(s[i], **kwargs)
            # Lemmatizer.
            if lemmata:
                s[i] = self.find_lemmata(s[i], **kwargs)
        # Slash-formatted tagged string.
        # With collapse=False (or split=True), returns raw list
        # (this output is not usable by tree.Text).
        if not kwargs.get("collapse", True) \
            or kwargs.get("split", False):
            return s
        # Construct TaggedString.format.
        # (this output is usable by tree.Text).
        format = ["word"]
        if tags:
            format.append("part-of-speech")
        if chunks:
            format.extend(("chunk", "preposition"))
        if relations:
            format.append("relation")
        if lemmata:
            format.append("lemma")
        # Collapse raw list.
        # Sentences are separated by newlines, tokens by spaces, tags by slashes.
        # Slashes in words are encoded with &slash;
        for i in range(len(s)):
            for j in range(len(s[i])):
                s[i][j][0] = s[i][j][0].replace("/", "&slash;")
                s[i][j] = "/".join(s[i][j])
            s[i] = " ".join(s[i])
        s = "\n".join(s)
        s = TaggedString(s, format, language=kwargs.get("language", self.language))
        return s

#--- TAGGED STRING ---------------------------------------------------------------------------------
# Pattern.parse() returns a TaggedString: a Unicode string with "tags" and "language" attributes.
# The pattern.text.tree.Text class uses this attribute to determine the token format and
# transform the tagged string to a parse tree of nested Sentence, Chunk and Word objects.

TOKENS = "tokens"

class TaggedString(unicode):

    def __new__(self, string, tags=["word"], language=None):
        """ Unicode string with tags and language attributes.
            For example: TaggedString("cat/NN/NP", tags=["word", "pos", "chunk"]).
        """
        # From a TaggedString:
        if isinstance(string, unicode) and hasattr(string, "tags"):
            tags, language = string.tags, string.language
        # From a TaggedString.split(TOKENS) list:
        if isinstance(string, list):
            string = [[[x.replace("/", "&slash;") for x in token] for token in s] for s in string]
            string = "\n".join(" ".join("/".join(token) for token in s) for s in string)
        s = unicode.__new__(self, string)
        s.tags = list(tags)
        s.language = language
        return s

    def split(self, sep=TOKENS):
        """ Returns a list of sentences, where each sentence is a list of tokens,
            where each token is a list of word + tags.
        """
        if sep != TOKENS:
            return unicode.split(self, sep)
        if len(self) == 0:
            return []
        return [[[x.replace("&slash;", "/") for x in token.split("/")]
            for token in sentence.split(" ")]
                for sentence in unicode.split(self, "\n")]

#--- UNIVERSAL TAGSET ------------------------------------------------------------------------------
# The default part-of-speech tagset used in Pattern is Penn Treebank II.
# However, not all languages are well-suited to Penn Treebank (which was developed for English).
# As more languages are implemented, this is becoming more problematic.
#
# A universal tagset is proposed by Slav Petrov (2012):
# http://www.petrovi.de/data/lrec.pdf
#
# Subclasses of Parser should start implementing
# Parser.parse(tagset=UNIVERSAL) with a simplified tagset.
# The names of the constants correspond to Petrov's naming scheme, while
# the value of the constants correspond to Penn Treebank.

UNIVERSAL = "universal"

NOUN, VERB, ADJ, ADV, PRON, DET, PREP, ADP, NUM, CONJ, INTJ, PRT, PUNC, X = \
    "NN", "VB", "JJ", "RB", "PR", "DT", "PP", "PP", "NO", "CJ", "UH", "PT", ".", "X"

def penntreebank2universal(token, tag):
    """ Returns a (token, tag)-tuple with a simplified universal part-of-speech tag.
    """
    if tag.startswith(("NNP-", "NNPS-")):
        return (token, "%s-%s" % (NOUN, tag.split("-")[-1]))
    if tag in ("NN", "NNS", "NNP", "NNPS", "NP"):
        return (token, NOUN)
    if tag in ("MD", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ"):
        return (token, VERB)
    if tag in ("JJ", "JJR", "JJS"):
        return (token, ADJ)
    if tag in ("RB", "RBR", "RBS", "WRB"):
        return (token, ADV)
    if tag in ("PRP", "PRP$", "WP", "WP$"):
        return (token, PRON)
    if tag in ("DT", "PDT", "WDT", "EX"):
        return (token, DET)
    if tag in ("IN",):
        return (token, PREP)
    if tag in ("CD",):
        return (token, NUM)
    if tag in ("CC",):
        return (token, CONJ)
    if tag in ("UH",):
        return (token, INTJ)
    if tag in ("POS", "RP", "TO"):
        return (token, PRT)
    if tag in ("SYM", "LS", ".", "!", "?", ",", ":", "(", ")", "\"", "#", "$"):
        return (token, PUNC)
    return (token, X)

#--- TOKENIZER -------------------------------------------------------------------------------------

TOKEN = re.compile(r"(\S+)\s")

# Handle common punctuation marks.
PUNCTUATION = \
punctuation = ".,;:!?()[]{}`''\"@#$^&*+-|=~_"

# Handle common abbreviations.
ABBREVIATIONS = abbreviations = set((
    "a.", "adj.", "adv.", "al.", "a.m.", "art.", "c.", "capt.", "cert.", "cf.", "col.", "Col.", 
    "comp.", "conf.", "def.", "Dep.", "Dept.", "Dr.", "dr.", "ed.", "e.g.", "esp.", "etc.", "ex.", 
    "f.", "fig.", "gen.", "id.", "i.e.", "int.", "l.", "m.", "Med.", "Mil.", "Mr.", "n.", "n.q.", 
    "orig.", "pl.", "pred.", "pres.", "p.m.", "ref.", "v.", "vs.", "w/"
))

RE_ABBR1 = re.compile("^[A-Za-z]\.$")       # single letter, "T. De Smedt"
RE_ABBR2 = re.compile("^([A-Za-z]\.)+$")    # alternating letters, "U.S."
RE_ABBR3 = re.compile("^[A-Z][" + "|".join( # capital followed by consonants, "Mr."
        "bcdfghjklmnpqrstvwxz") + "]+.$")

# Handle emoticons.
EMOTICONS = { # (facial expression, sentiment)-keys
    ("love" , +1.00): set(("<3", u"♥", u"❤")),
    ("grin" , +1.00): set((">:D", ":-D", ":D", "=-D", "=D", "X-D", "x-D", "XD", "xD", "8-D")),
    ("taunt", +0.75): set((">:P", ":-P", ":P", ":-p", ":p", ":-b", ":b", ":c)", ":o)", ":^)")),
    ("smile", +0.50): set((">:)", ":-)", ":)", "=)", "=]", ":]", ":}", ":>", ":3", "8)", "8-)")),
    ("wink" , +0.25): set((">;]", ";-)", ";)", ";-]", ";]", ";D", ";^)", "*-)", "*)")),
    ("gasp" , +0.05): set((">:o", ":-O", ":O", ":o", ":-o", "o_O", "o.O", u"°O°", u"°o°")),
    ("worry", -0.25): set((">:/",  ":-/", ":/", ":\\", ">:\\", ":-.", ":-s", ":s", ":S", ":-S", ">.>")),
    ("frown", -0.75): set((">:[", ":-(", ":(", "=(", ":-[", ":[", ":{", ":-<", ":c", ":-c", "=/")),
    ("cry"  , -1.00): set((":'(", ":'''(", ";'("))
}

RE_EMOTICONS = [r" ?".join(map(re.escape, e)) for v in EMOTICONS.values() for e in v]
RE_EMOTICONS = re.compile(r"(%s)($|\s)" % "|".join(RE_EMOTICONS))

# Handle sarcasm punctuation (!).
RE_SARCASM = re.compile(r"\( ?\! ?\)")

# Handle common contractions.
replacements = {
     "'d": " 'd",
     "'m": " 'm",
     "'s": " 's",
    "'ll": " 'll",
    "'re": " 're",
    "'ve": " 've",
    "n't": " n't"
}

# Handle paragraph line breaks (\n\n marks end of sentence).
EOS = "END-OF-SENTENCE"

def find_tokens(string, punctuation=PUNCTUATION, abbreviations=ABBREVIATIONS, replace=replacements, linebreak=r"\n{2,}"):
    """ Returns a list of sentences. Each sentence is a space-separated string of tokens (words).
        Handles common cases of abbreviations (e.g., etc., ...).
        Punctuation marks are split from other words. Periods (or ?!) mark the end of a sentence.
        Headings without an ending period are inferred by line breaks.
    """
    # Handle periods separately.
    punctuation = tuple(punctuation.replace(".", ""))
    # Handle replacements (contractions).
    for a, b in replace.items():
        string = re.sub(a, b, string)
    # Handle Unicode quotes.
    if isinstance(string, unicode):
        string = string.replace(u"“", u" “ ")
        string = string.replace(u"”", u" ” ")
        string = string.replace(u"‘", u" ‘ ")
        string = string.replace(u"’", u" ’ ")
    # Collapse whitespace.
    string = re.sub("\r\n", "\n", string)
    string = re.sub(linebreak, " %s " % EOS, string)
    string = re.sub(r"\s+", " ", string)
    tokens = []
    # Handle punctuation marks.
    for t in TOKEN.findall(string+" "):
        if len(t) > 0:
            tail = []
            while t.startswith(punctuation) and \
              not t in replace:
                # Split leading punctuation.
                if t.startswith(punctuation):
                    tokens.append(t[0]); t=t[1:]
            while t.endswith(punctuation+(".",)) and \
              not t in replace:
                # Split trailing punctuation.
                if t.endswith(punctuation):
                    tail.append(t[-1]); t=t[:-1]
                # Split ellipsis (...) before splitting period.
                if t.endswith("..."):
                    tail.append("..."); t=t[:-3].rstrip(".")
                # Split period (if not an abbreviation).
                if t.endswith("."):
                    if t in abbreviations or \
                      RE_ABBR1.match(t) is not None or \
                      RE_ABBR2.match(t) is not None or \
                      RE_ABBR3.match(t) is not None:
                        break
                    else:
                        tail.append(t[-1]); t=t[:-1]
            if t != "":
                tokens.append(t)
            tokens.extend(reversed(tail))
    # Handle sentence breaks (periods, quotes, parenthesis).
    sentences, i, j = [[]], 0, 0
    while j < len(tokens):
        if tokens[j] in ("...", ".", "!", "?", EOS):
            while j < len(tokens) \
              and tokens[j] in ("'", "\"", u"”", u"’", "...", ".", "!", "?", ")", EOS):
                if tokens[j] in ("'", "\"") and sentences[-1].count(tokens[j]) % 2 == 0:
                    break # Balanced quotes.
                j += 1
            sentences[-1].extend(t for t in tokens[i:j] if t != EOS)
            sentences.append([])
            i = j
        j += 1
    # Handle emoticons.
    sentences[-1].extend(tokens[i:j])
    sentences = (" ".join(s) for s in sentences if len(s) > 0)
    sentences = (RE_SARCASM.sub("(!)", s) for s in sentences)
    sentences = [RE_EMOTICONS.sub(
        lambda m: m.group(1).replace(" ", "") + m.group(2), s) for s in sentences]
    return sentences

#--- PART-OF-SPEECH TAGGER -------------------------------------------------------------------------

# Unknown words are recognized as numbers if they contain only digits and -,.:/%$
CD = re.compile(r"^[0-9\-\,\.\:\/\%\$]+$")

def _suffix_rules(token, tag="NN"):
    """ Default morphological tagging rules for English, based on word suffixes.
    """
    if isinstance(token, (list, tuple)):
        token, tag = token
    if token.endswith("ing"):
        tag = "VBG"
    if token.endswith("ly"):
        tag = "RB"
    if token.endswith("s") and not token.endswith(("is", "ous", "ss")):
        tag = "NNS"
    if token.endswith(("able", "al", "ful", "ible", "ient", "ish", "ive", "less", "tic", "ous")) or "-" in token:
        tag = "JJ"
    if token.endswith("ed"):
        tag = "VBN"
    if token.endswith(("ate", "ify", "ise", "ize")):
        tag = "VBP"
    return [token, tag]

def find_tags(tokens, lexicon={}, model=None, morphology=None, context=None, entities=None, default=("NN", "NNP", "CD"), language="en", map=None, **kwargs):
    """ Returns a list of [token, tag]-items for the given list of tokens:
        ["The", "cat", "purs"] => [["The", "DT"], ["cat", "NN"], ["purs", "VB"]]
        Words are tagged using the given lexicon of (word, tag)-items.
        Unknown words are tagged NN by default.
        Unknown words that start with a capital letter are tagged NNP (unless language="de").
        Unknown words that consist only of digits and punctuation marks are tagged CD.
        Unknown words are then improved with morphological rules.
        All words are improved with contextual rules.
        If a model is given, uses model for unknown words instead of morphology and context.
        If map is a function, it is applied to each (token, tag) after applying all rules.
    """
    tagged = []
    # Tag known words.
    for i, token in enumerate(tokens):
        tagged.append([token, lexicon.get(token, i == 0 and lexicon.get(token.lower()) or None)])
    # Tag unknown words.
    for i, (token, tag) in enumerate(tagged):
        prev, next = (None, None), (None, None)
        if i > 0:
            prev = tagged[i-1]
        if i < len(tagged) - 1:
            next = tagged[i+1]
        if tag is None or token in (model is not None and model.unknown or ()):
            # Use language model (i.e., SLP).
            if model is not None:
                tagged[i] = model.apply([token, None], prev, next)
            # Use NNP for capitalized words (except in German).
            elif token.istitle() and language != "de":
                tagged[i] = [token, default[1]]
            # Use CD for digits and numbers.
            elif CD.match(token) is not None:
                tagged[i] = [token, default[2]]
            # Use suffix rules (e.g., -ly = RB).
            elif morphology is not None:
                tagged[i] = morphology.apply([token, default[0]], prev, next)
            # Use suffix rules (English default).
            elif language == "en":
                tagged[i] = _suffix_rules([token, default[0]])
            # Use most frequent tag (NN).
            else:
                tagged[i] = [token, default[0]]
    # Tag words by context.
    if context is not None and model is None:
        tagged = context.apply(tagged)
    # Tag named entities.
    if entities is not None:
        tagged = entities.apply(tagged)
    # Map tags with a custom function.
    if map is not None:
        tagged = [list(map(token, tag)) or [token, default[0]] for token, tag in tagged]
    return tagged

#--- PHRASE CHUNKER --------------------------------------------------------------------------------

SEPARATOR = "/"

NN = r"NN|NNS|NNP|NNPS|NNPS?\-[A-Z]{3,4}|PR|PRP|PRP\$"
VB = r"VB|VBD|VBG|VBN|VBP|VBZ"
JJ = r"JJ|JJR|JJS"
RB = r"(?<!W)RB|RBR|RBS"
CC = r"CC|CJ"

# Chunking rules.
# CHUNKS[0] = Germanic: RB + JJ precedes NN ("the round table").
# CHUNKS[1] = Romance: RB + JJ precedes or follows NN ("la table ronde", "une jolie fille").
CHUNKS = [[
    # Germanic languages: en, de, nl, ...
    (  "NP", r"((NN)/)* ((DT|CD|CC)/)* ((RB|JJ)/)* (((JJ)/(CC|,)/)*(JJ)/)* ((NN)/)+"),
    (  "VP", r"(((MD|TO|RB)/)* ((VB)/)+ ((RP)/)*)+"),
    (  "VP", r"((MD)/)"),
    (  "PP", r"((IN|PP)/)+"),
    ("ADJP", r"((RB|JJ)/)* ((JJ)/,/)* ((JJ)/(CC)/)* ((JJ)/)+"),
    ("ADVP", r"((RB)/)+"),
], [
    # Romance languages: es, fr, it, ...
    (  "NP", r"((NN)/)* ((DT|CD|CC)/)* ((RB|JJ|,)/)* (((JJ)/(CC|,)/)*(JJ)/)* ((NN)/)+ ((RB|JJ)/)*"),
    (  "VP", r"(((MD|TO|RB)/)* ((VB)/)+ ((RP)/)* ((RB)/)*)+"),
    (  "VP", r"((MD)/)"),
    (  "PP", r"((IN|PP)/)+"),
    ("ADJP", r"((RB|JJ)/)* ((JJ)/,/)* ((JJ)/(CC)/)* ((JJ)/)+"),
    ("ADVP", r"((RB)/)+"),
]]

for i in (0, 1):
    for j, (tag, s) in enumerate(CHUNKS[i]):
        s = s.replace("NN", NN)
        s = s.replace("VB", VB)
        s = s.replace("JJ", JJ)
        s = s.replace("RB", RB)
        s = s.replace(" ", "")
        s = re.compile(s)
        CHUNKS[i][j] = (tag, s)

# Handle ADJP before VP, 
# so that RB prefers next ADJP over previous VP.
CHUNKS[0].insert(1, CHUNKS[0].pop(3))
CHUNKS[1].insert(1, CHUNKS[1].pop(3))

def find_chunks(tagged, language="en"):
    """ The input is a list of [token, tag]-items.
        The output is a list of [token, tag, chunk]-items:
        The/DT nice/JJ fish/NN is/VBZ dead/JJ ./. =>
        The/DT/B-NP nice/JJ/I-NP fish/NN/I-NP is/VBZ/B-VP dead/JJ/B-ADJP ././O
    """
    chunked = [x for x in tagged]
    tags = "".join("%s%s" % (tag, SEPARATOR) for token, tag in tagged)
    # Use Germanic or Romance chunking rules according to given language.
    for tag, rule in CHUNKS[int(language in ("ca", "es", "pt", "fr", "it", "pt", "ro"))]:
        for m in rule.finditer(tags):
            # Find the start of chunks inside the tags-string.
            # Number of preceding separators = number of preceding tokens.
            i = m.start()
            j = tags[:i].count(SEPARATOR)
            n = m.group(0).count(SEPARATOR)
            for k in range(j, j+n):
                if len(chunked[k]) == 3:
                    continue
                if len(chunked[k]) < 3:
                    # A conjunction or comma cannot be start of a chunk.
                    if k == j and chunked[k][1] in ("CC", "CJ", ","):
                        j += 1
                    # Mark first token in chunk with B-.
                    elif k == j:
                        chunked[k].append("B-" + tag)
                    # Mark other tokens in chunk with I-.
                    else:
                        chunked[k].append("I-" + tag)
    # Mark chinks (tokens outside of a chunk) with O-.
    for chink in filter(lambda x: len(x) < 3, chunked):
        chink.append("O")
    # Post-processing corrections.
    for i, (word, tag, chunk) in enumerate(chunked):
        if tag.startswith("RB") and chunk == "B-NP":
            # "Perhaps you" => ADVP + NP
            # "Really nice work" => NP
            # "Really, nice work" => ADVP + O + NP
            if i < len(chunked)-1 and not chunked[i+1][1].startswith("JJ"):
                chunked[i+0][2] = "B-ADVP"
                chunked[i+1][2] = "B-NP"
            if i < len(chunked)-1 and chunked[i+1][1] in ("CC", "CJ", ","):
                chunked[i+1][2] = "O"
            if i < len(chunked)-2 and chunked[i+1][2] == "O":
                chunked[i+2][2] = "B-NP"
    return chunked

def find_prepositions(chunked):
    """ The input is a list of [token, tag, chunk]-items.
        The output is a list of [token, tag, chunk, preposition]-items.
        PP-chunks followed by NP-chunks make up a PNP-chunk.
    """
    # Tokens that are not part of a preposition just get the O-tag.
    for ch in chunked:
        ch.append("O")
    for i, chunk in enumerate(chunked):
        if chunk[2].endswith("PP") and chunk[-1] == "O":
            # Find PP followed by other PP, NP with nouns and pronouns, VP with a gerund.
            if i < len(chunked)-1 and \
             (chunked[i+1][2].endswith(("NP", "PP")) or \
              chunked[i+1][1] in ("VBG", "VBN")):
                chunk[-1] = "B-PNP"
                pp = True
                for ch in chunked[i+1:]:
                    if not (ch[2].endswith(("NP", "PP")) or ch[1] in ("VBG", "VBN")):
                        break
                    if ch[2].endswith("PP") and pp:
                        ch[-1] = "I-PNP"
                    if not ch[2].endswith("PP"):
                        ch[-1] = "I-PNP"
                        pp = False
    return chunked

#--- SEMANTIC ROLE LABELER -------------------------------------------------------------------------
# Naive approach.

BE = dict.fromkeys(("be", "am", "are", "is", "being", "was", "were", "been"), True)
GO = dict.fromkeys(("go", "goes", "going", "went"), True)

def find_relations(chunked):
    """ The input is a list of [token, tag, chunk]-items.
        The output is a list of [token, tag, chunk, relation]-items.
        A noun phrase preceding a verb phrase is perceived as sentence subject.
        A noun phrase following a verb phrase is perceived as sentence object.
    """
    tag = lambda token: token[2].split("-")[-1] # B-NP => NP
    # Group successive tokens with the same chunk-tag.
    chunks = []
    for token in chunked:
        if len(chunks) == 0 \
        or token[2].startswith("B-") \
        or tag(token) != tag(chunks[-1][-1]):
            chunks.append([])
        chunks[-1].append(token+["O"])
    # If a VP is preceded by a NP, the NP is tagged as NP-SBJ-(id).
    # If a VP is followed by a NP, the NP is tagged as NP-OBJ-(id).
    # Chunks that are not part of a relation get an O-tag.
    id = 0
    for i, chunk in enumerate(chunks):
        if tag(chunk[-1]) == "VP" and i > 0 and tag(chunks[i-1][-1]) == "NP":
            if chunk[-1][-1] == "O":
                id += 1
            for token in chunk:
                token[-1] = "VP-" + str(id)
            for token in chunks[i-1]:
                token[-1] += "*NP-SBJ-" + str(id)
                token[-1] = token[-1].lstrip("O-*")
        if tag(chunk[-1]) == "VP" and i < len(chunks)-1 and tag(chunks[i+1][-1]) == "NP":
            if chunk[-1][-1] == "O":
                id += 1
            for token in chunk:
                token[-1] = "VP-" + str(id)
            for token in chunks[i+1]:
                token[-1] = "*NP-OBJ-" + str(id)
                token[-1] = token[-1].lstrip("O-*")
    # This is more a proof-of-concept than useful in practice:
    # PP-LOC = be + in|at + the|my
    # PP-DIR = go + to|towards + the|my
    for i, chunk in enumerate(chunks):
        if 0 < i < len(chunks)-1 and len(chunk) == 1 and chunk[-1][-1] == "O":
            t0, t1, t2 = chunks[i-1][-1], chunks[i][0], chunks[i+1][0] # previous / current / next
            if tag(t1) == "PP" and t2[1] in ("DT", "PR", "PRP$"):
                if t0[0] in BE and t1[0] in ("in", "at")      : t1[-1] = "PP-LOC"
                if t0[0] in GO and t1[0] in ("to", "towards") : t1[-1] = "PP-DIR"
    related = []; [related.extend(chunk) for chunk in chunks]
    return related

#--- KEYWORDS EXTRACTION ---------------------------------------------------------------------------

def find_keywords(string, parser, top=10, frequency={}, **kwargs):
    """ Returns a sorted list of keywords in the given string.
        The given parser (e.g., pattern.en.parser) is used to identify noun phrases.
        The given frequency dictionary can be a reference corpus,
        with relative document frequency (df, 0.0-1.0) for each lemma, 
        e.g., {"the": 0.8, "cat": 0.1, ...}
    """
    lemmata = kwargs.pop("lemmata", kwargs.pop("stem", True))
    # Parse the string and extract noun phrases (NP).
    chunks = []
    wordcount = 0
    for sentence in parser.parse(string, chunks=True, lemmata=lemmata).split():
        for w in sentence: # ["cats", "NNS", "I-NP", "O", "cat"]
            if w[2] == "B-NP":
                chunks.append([w])
                wordcount += 1
            elif w[2] == "I-NP" and w[1][:3] == chunks[-1][-1][1][:3] == "NNP":
                chunks[-1][-1][+0] += " " + w[+0] # Collapse NNPs: "Ms Kitty".
                chunks[-1][-1][-1] += " " + w[-1]
            elif w[2] == "I-NP":
                chunks[-1].append(w)
                wordcount += 1
    # Rate the nouns in noun phrases.
    m = {}
    for i, chunk in enumerate(chunks):
        head = True
        if parser.language not in ("ca", "es", "pt", "fr", "it", "pt", "ro"):
            # Head of "cat hair" => "hair".
            # Head of "poils de chat" => "poils".
            chunk = list(reversed(chunk))
        for w in chunk:
            if w[1].startswith("NN"):
                if lemmata:
                    k = w[-1]
                else:
                    k = w[0].lower()
                if not k in m:
                    m[k] = [0.0, set(), 1.0, 1.0, 1.0]
                # Higher score for chunks that appear more frequently.
                m[k][0] += 1 / float(wordcount)
                # Higher score for chunks that appear in more contexts (semantic centrality).
                m[k][1].add(" ".join(map(lambda x: x[0], chunk)).lower())
                # Higher score for chunks at the start (25%) of the text.
                m[k][2] += 1 if float(i) / len(chunks) <= 0.25 else 0
                # Higher score for chunks not in a prepositional phrase.
                m[k][3] += 1 if w[3] == "O" else 0
                # Higher score for chunk head.
                m[k][4] += 1 if head else 0
                head = False
    # Rate tf-idf if a frequency dict is given.
    for k in m:
        if frequency:
            df = frequency.get(k, 0.0)
            df = max(df, 1e-10)
            df = log(1.0 / df, 2.71828)
        else:
            df = 1.0
        m[k][0] = max(1e-10, m[k][0] * df)
        m[k][1] = 1 + float(len(m[k][1]))
    # Sort candidates alphabetically by total score
    # The harmonic mean will emphasize tf-idf score.
    hmean = lambda a: len(a) / sum(1.0 / x for x in a)
    m = [(hmean(m[k]), k) for k in m]
    m = sorted(m, key=lambda x: x[1])
    m = sorted(m, key=lambda x: x[0], reverse=True)
    m = [k for score, k in m]
    return m[:top]

#### COMMAND LINE ##################################################################################
# The commandline() function enables command line support for a Parser.
# The following code can be added to pattern.en, for example:
#
# from pattern.text import Parser, commandline
# parse = Parser(lexicon=LEXICON).parse
# if __name__ == "main":
#     commandline(parse)
#
# The parser is then accessible from the command line:
# python -m pattern.en.parser xml -s "Hello, my name is Dr. Sbaitso. Nice to meet you." -OTCLI

def commandline(parse=Parser().parse):
    import optparse
    import codecs
    p = optparse.OptionParser()
    p.add_option("-f", "--file",      dest="file",      action="store",      help="text file to parse",   metavar="FILE")
    p.add_option("-s", "--string",    dest="string",    action="store",      help="text string to parse", metavar="STRING")
    p.add_option("-O", "--tokenize",  dest="tokenize",  action="store_true", help="tokenize the input")
    p.add_option("-T", "--tags",      dest="tags",      action="store_true", help="parse part-of-speech tags")
    p.add_option("-C", "--chunks",    dest="chunks",    action="store_true", help="parse chunk tags")
    p.add_option("-R", "--relations", dest="relations", action="store_true", help="find verb/predicate relations")
    p.add_option("-L", "--lemmata",   dest="lemmata",   action="store_true", help="find word lemmata")
    p.add_option("-e", "--encoding",  dest="encoding",  action="store_true", help="character encoding", default="utf-8")
    p.add_option("-v", "--version",   dest="version",   action="store_true", help="version info")
    o, arguments = p.parse_args()
    # Version info.
    if o.version:
        sys.path.insert(0, os.path.join(MODULE, "..", ".."))
        from pattern import __version__
        print(__version__)
        sys.path.pop(0)
    # Either a text file (-f) or a text string (-s) must be supplied.
    s = o.file and codecs.open(o.file, "r", o.encoding).read() or o.string
    # The given text can be parsed in two modes:
    # - implicit: parse everything (tokenize, tag/chunk, find relations, lemmatize).
    # - explicit: define what to parse manually.
    if s:
        explicit = False
        for option in [o.tokenize, o.tags, o.chunks, o.relations, o.lemmata]:
            if option is not None: explicit=True; break
        if not explicit:
            a = {"encoding": o.encoding }
        else:
            a = {"tokenize": o.tokenize  or False,
                     "tags": o.tags      or False,
                   "chunks": o.chunks    or False,
                "relations": o.relations or False,
                  "lemmata": o.lemmata   or False,
                 "encoding": o.encoding }
        s = parse(s, **a)
        # The output can be either slash-formatted string or XML.
        if "xml" in arguments:
            s = Tree(s, s.tags).xml
        print(encode_utf8(s))

#### VERBS #########################################################################################

#--- VERB TENSES -----------------------------------------------------------------------------------
# Conjugation is the inflection of verbs by tense, person, number, mood and aspect.

# VERB TENSE:
INFINITIVE, PRESENT, PAST, FUTURE = \
    INF, PRES, PST, FUT = \
        "infinitive", "present", "past", "future"

# VERB PERSON:
# 1st person = I or we (plural).
# 2nd person = you.
# 3rd person = he, she, it or they (plural).
FIRST, SECOND, THIRD = \
    1, 2, 3

# VERB NUMBER:
# singular number = I, you, he, she, it.
#   plural number = we, you, they.
SINGULAR, PLURAL = \
    SG, PL = \
        "singular", "plural"

# VERB MOOD:
#  indicative mood = a fact: "the cat meowed".
#  imperative mood = a command: "meow!".
# conditional mood = a hypothesis: "a cat *will* meow *if* it is hungry".
# subjunctive mood = a wish, possibility or necessity: "I *wish* the cat *would* stop meowing".
INDICATIVE, IMPERATIVE, CONDITIONAL, SUBJUNCTIVE = \
    IND, IMP, COND, SJV = \
        "indicative", "imperative", "conditional", "subjunctive"

# VERB ASPECT:
# imperfective aspect = a habitual or ongoing action: "it was midnight; the cat meowed".
#   perfective aspect = a momentary or completed action: "I flung my pillow at the cat".
#  progressive aspect = a incomplete action in progress: "the cat was meowing".
# Note: the progressive aspect is a subtype of the imperfective aspect.
IMPERFECTIVE, PERFECTIVE, PROGRESSIVE = \
    IPFV, PFV, PROG = \
        "imperfective", "perfective", "progressive"

# Imperfect = past tense + imperfective aspect.
# Preterite = past tense + perfective aspect.
IMPERFECT = "imperfect"
PRETERITE = "preterite"

# Participle = present tense  + progressive aspect.
PARTICIPLE, GERUND = "participle", "gerund"

# Continuous aspect ≈ progressive aspect.
CONTINUOUS = CONT = "continuous"

_ = None # prettify the table =>

# Unique index per tense (= tense + person + number + mood + aspect + negated? + aliases).
# The index is used to describe the format of the verb lexicon file.
# The aliases can be passed to Verbs.conjugate() and Tenses.__contains__().
TENSES = {
   None: (None, _,  _,    _,    _, False, (None   ,)), #       ENGLISH   SPANISH   GERMAN    DUTCH     FRENCH
     0 : ( INF, _,  _,    _,    _, False, ("inf"  ,)), #       to be     ser       sein      zijn      être
     1 : (PRES, 1, SG,  IND, IPFV, False, ("1sg"  ,)), #     I am        soy       bin       ben       suis
     2 : (PRES, 2, SG,  IND, IPFV, False, ("2sg"  ,)), #   you are       eres      bist      bent      es
     3 : (PRES, 3, SG,  IND, IPFV, False, ("3sg"  ,)), # (s)he is        es        ist       is        est
     4 : (PRES, 1, PL,  IND, IPFV, False, ("1pl"  ,)), #    we are       somos     sind      zijn      sommes
     5 : (PRES, 2, PL,  IND, IPFV, False, ("2pl"  ,)), #   you are       sois      seid      zijn      êtes
     6 : (PRES, 3, PL,  IND, IPFV, False, ("3pl"  ,)), #  they are       son       sind      zijn      sont
     7 : (PRES, _, PL,  IND, IPFV, False, ( "pl"  ,)), #       are
     8 : (PRES, _,  _,  IND, PROG, False, ("part" ,)), #       being     siendo              zijnd     étant
     9 : (PRES, 1, SG,  IND, IPFV, True,  ("1sg-" ,)), #     I am not
    10 : (PRES, 2, SG,  IND, IPFV, True,  ("2sg-" ,)), #   you aren't
    11 : (PRES, 3, SG,  IND, IPFV, True,  ("3sg-" ,)), # (s)he isn't
    12 : (PRES, 1, PL,  IND, IPFV, True,  ("1pl-" ,)), #    we aren't
    13 : (PRES, 2, PL,  IND, IPFV, True,  ("2pl-" ,)), #   you aren't
    14 : (PRES, 3, PL,  IND, IPFV, True,  ("3pl-" ,)), #  they aren't
    15 : (PRES, _, PL,  IND, IPFV, True,  ( "pl-" ,)), #       aren't
    16 : (PRES, _,  _,  IND, IPFV, True,  (   "-" ,)), #       isn't
    17 : ( PST, 1, SG,  IND, IPFV, False, ("1sgp" ,)), #     I was       era       war       was       étais
    18 : ( PST, 2, SG,  IND, IPFV, False, ("2sgp" ,)), #   you were      eras      warst     was       étais
    19 : ( PST, 3, SG,  IND, IPFV, False, ("3sgp" ,)), # (s)he was       era       war       was       était
    20 : ( PST, 1, PL,  IND, IPFV, False, ("1ppl" ,)), #    we were      éramos    waren     waren     étions
    21 : ( PST, 2, PL,  IND, IPFV, False, ("2ppl" ,)), #   you were      erais     wart      waren     étiez
    22 : ( PST, 3, PL,  IND, IPFV, False, ("3ppl" ,)), #  they were      eran      waren     waren     étaient
    23 : ( PST, _, PL,  IND, IPFV, False, ( "ppl" ,)), #       were
    24 : ( PST, _,  _,  IND, PROG, False, ("ppart",)), #       been      sido      gewesen   geweest   été
    25 : ( PST, _,  _,  IND, IPFV, False, (   "p" ,)), #       was
    26 : ( PST, 1, SG,  IND, IPFV, True,  ("1sgp-",)), #     I wasn't
    27 : ( PST, 2, SG,  IND, IPFV, True,  ("2sgp-",)), #   you weren't
    28 : ( PST, 3, SG,  IND, IPFV, True,  ("3sgp-",)), # (s)he wasn't
    29 : ( PST, 1, PL,  IND, IPFV, True,  ("1ppl-",)), #    we weren't
    30 : ( PST, 2, PL,  IND, IPFV, True,  ("2ppl-",)), #   you weren't
    31 : ( PST, 3, PL,  IND, IPFV, True,  ("3ppl-",)), #  they weren't
    32 : ( PST, _, PL,  IND, IPFV, True,  ( "ppl-",)), #       weren't
    33 : ( PST, _,  _,  IND, IPFV, True,  ( "p-"  ,)), #       wasn't
    34 : ( PST, 1, SG,  IND,  PFV, False, ("1sg+" ,)), #     I           fui                           fus
    35 : ( PST, 2, SG,  IND,  PFV, False, ("2sg+" ,)), #   you           fuiste                        fus
    36 : ( PST, 3, SG,  IND,  PFV, False, ("3sg+" ,)), # (s)he           fue                           fut
    37 : ( PST, 1, PL,  IND,  PFV, False, ("1pl+" ,)), #    we           fuimos                        fûmes
    38 : ( PST, 2, PL,  IND,  PFV, False, ("2pl+" ,)), #   you           fuisteis                      fûtes
    39 : ( PST, 3, PL,  IND,  PFV, False, ("3pl+" ,)), #  they           fueron                        furent
    40 : ( FUT, 1, SG,  IND, IPFV, False, ("1sgf" ,)), #     I           seré                          serai
    41 : ( FUT, 2, SG,  IND, IPFV, False, ("2sgf" ,)), #   you           serás                         seras
    42 : ( FUT, 3, SG,  IND, IPFV, False, ("3sgf" ,)), # (s)he           será                          sera
    43 : ( FUT, 1, PL,  IND, IPFV, False, ("1plf" ,)), #    we           seremos                       serons
    44 : ( FUT, 2, PL,  IND, IPFV, False, ("2plf" ,)), #   you           seréis                        serez
    45 : ( FUT, 3, PL,  IND, IPFV, False, ("3plf" ,)), #  they           serán                         seron
    46 : (PRES, 1, SG, COND, IPFV, False, ("1sg->",)), #     I           sería                         serais
    47 : (PRES, 2, SG, COND, IPFV, False, ("2sg->",)), #   you           serías                        serais
    48 : (PRES, 3, SG, COND, IPFV, False, ("3sg->",)), # (s)he           sería                         serait
    49 : (PRES, 1, PL, COND, IPFV, False, ("1pl->",)), #    we           seríamos                      serions
    50 : (PRES, 2, PL, COND, IPFV, False, ("2pl->",)), #   you           seríais                       seriez
    51 : (PRES, 3, PL, COND, IPFV, False, ("3pl->",)), #  they           serían                        seraient
    52 : (PRES, 2, SG,  IMP, IPFV, False, ("2sg!" ,)), #   you           sé        sei                 sois
    521: (PRES, 3, SG,  IMP, IPFV, False, ("3sg!" ,)), # (s)he
    53 : (PRES, 1, PL,  IMP, IPFV, False, ("1pl!" ,)), #    we                     seien               soyons
    54 : (PRES, 2, PL,  IMP, IPFV, False, ("2pl!" ,)), #   you           sed       seid                soyez
    541: (PRES, 3, PL,  IMP, IPFV, False, ("3pl!" ,)), #   you
    55 : (PRES, 1, SG,  SJV, IPFV, False, ("1sg?" ,)), #     I           sea       sei                 sois
    56 : (PRES, 2, SG,  SJV, IPFV, False, ("2sg?" ,)), #   you           seas      seist               sois
    57 : (PRES, 3, SG,  SJV, IPFV, False, ("3sg?" ,)), # (s)he           sea       sei                 soit
    58 : (PRES, 1, PL,  SJV, IPFV, False, ("1pl?" ,)), #    we           seamos    seien               soyons
    59 : (PRES, 2, PL,  SJV, IPFV, False, ("2pl?" ,)), #   you           seáis     seiet               soyez
    60 : (PRES, 3, PL,  SJV, IPFV, False, ("3pl?" ,)), #  they           sean      seien               soient
    61 : (PRES, 1, SG,  SJV,  PFV, False, ("1sg?+",)), #     I
    62 : (PRES, 2, SG,  SJV,  PFV, False, ("2sg?+",)), #   you
    63 : (PRES, 3, SG,  SJV,  PFV, False, ("3sg?+",)), # (s)he
    64 : (PRES, 1, PL,  SJV,  PFV, False, ("1pl?+",)), #    we
    65 : (PRES, 2, PL,  SJV,  PFV, False, ("2pl?+",)), #   you
    66 : (PRES, 3, PL,  SJV,  PFV, False, ("3pl?+",)), #  they
    67 : ( PST, 1, SG,  SJV, IPFV, False, ("1sgp?",)), #     I           fuera     wäre                fusse
    68 : ( PST, 2, SG,  SJV, IPFV, False, ("2sgp?",)), #   you           fueras    wärest              fusses
    69 : ( PST, 3, SG,  SJV, IPFV, False, ("3sgp?",)), # (s)he           fuera     wäre                fût
    70 : ( PST, 1, PL,  SJV, IPFV, False, ("1ppl?",)), #    we           fuéramos  wären               fussions
    71 : ( PST, 2, PL,  SJV, IPFV, False, ("2ppl?",)), #   you           fuerais   wäret               fussiez
    72 : ( PST, 3, PL,  SJV, IPFV, False, ("3ppl?",)), #  they           fueran    wären               fussent
}

# Map tenses and aliases to unique index.
# Aliases include:
# - a short number: "s", "sg", "singular" => SINGULAR,
# - a short string: "1sg" => 1st person singular present,
# - a unique index:  1    => 1st person singular present,
# -  Penn treebank: "VBP" => 1st person singular present.
TENSES_ID = {}
TENSES_ID[INFINITIVE] = 0
for i, (tense, person, number, mood, aspect, negated, aliases) in TENSES.items():
    for a in aliases + (i,):
        TENSES_ID[i] = \
        TENSES_ID[a] = \
        TENSES_ID[(tense, person, number, mood, aspect, negated)] = i
    if number == SG:
        for sg in ("s", "sg", "singular"):
            TENSES_ID[(tense, person, sg, mood, aspect, negated)] = i
    if number == PL:
        for pl in ("p", "pl", "plural"):
            TENSES_ID[(tense, person, pl, mood, aspect, negated)] = i

# Map Penn Treebank tags to unique index.
for tag, tense in (
  ("VB",  0 ),  # infinitive
  ("VBP", 1 ),  # present 1 singular
  ("VBZ", 3 ),  # present 3 singular
  ("VBG", 8 ),  # present participle
  ("VBN", 24),  # past participle
  ("VBD", 25)): # past
    TENSES_ID[tag.lower()] = tense

# tense(tense=INFINITIVE)
# tense(tense=(PRESENT, 3, SINGULAR))
# tense(tense=PRESENT, person=3, number=SINGULAR, mood=INDICATIVE, aspect=IMPERFECTIVE, negated=False)
def tense_id(*args, **kwargs):
    """ Returns the tense id for a given (tense, person, number, mood, aspect, negated).
        Aliases and compound forms (e.g., IMPERFECT) are disambiguated.
    """
    # Unpack tense given as a tuple, e.g., tense((PRESENT, 1, SG)):
    if len(args) == 1 and isinstance(args[0], (list, tuple)):
         if args[0] not in ((PRESENT, PARTICIPLE), (PAST, PARTICIPLE)):
             args = args[0]
    # No parameters defaults to tense=INFINITIVE, tense=PRESENT otherwise.
    if len(args) == 0 and len(kwargs) == 0:
        t = INFINITIVE
    else:
        t = PRESENT
    # Set default values.
    tense   = kwargs.get("tense"  , args[0] if len(args) > 0 else t)
    person  = kwargs.get("person" , args[1] if len(args) > 1 else 3) or None
    number  = kwargs.get("number" , args[2] if len(args) > 2 else SINGULAR)
    mood    = kwargs.get("mood"   , args[3] if len(args) > 3 else INDICATIVE)
    aspect  = kwargs.get("aspect" , args[4] if len(args) > 4 else IMPERFECTIVE)
    negated = kwargs.get("negated", args[5] if len(args) > 5 else False)
    # Disambiguate wrong order of parameters.
    if mood in (PERFECTIVE, IMPERFECTIVE):
        mood, aspect = INDICATIVE, mood
    # Disambiguate INFINITIVE.
    # Disambiguate PARTICIPLE, IMPERFECT, PRETERITE.
    # These are often considered to be tenses but are in fact tense + aspect.
    if tense == INFINITIVE:
        person = number = mood = aspect = None; negated=False
    if tense in ((PRESENT, PARTICIPLE), PRESENT+PARTICIPLE, PARTICIPLE, GERUND):
        tense, aspect = PRESENT, PROGRESSIVE
    if tense in ((PAST, PARTICIPLE), PAST+PARTICIPLE):
        tense, aspect = PAST, PROGRESSIVE
    if tense == IMPERFECT:
        tense, aspect = PAST, IMPERFECTIVE
    if tense == PRETERITE:
        tense, aspect = PAST, PERFECTIVE
    if aspect in (CONTINUOUS, PARTICIPLE, GERUND):
        aspect = PROGRESSIVE
    if aspect == PROGRESSIVE:
        person = number = None
    # Disambiguate CONDITIONAL.
    # In Spanish, the conditional is regarded as an indicative tense.
    if tense == CONDITIONAL and mood == INDICATIVE:
        tense, mood = PRESENT, CONDITIONAL
    # Disambiguate aliases: "pl" =>
    # (PRESENT, None, PLURAL, INDICATIVE, IMPERFECTIVE, False).
    return TENSES_ID.get(tense.lower(),
           TENSES_ID.get((tense, person, number, mood, aspect, negated)))

tense = tense_id

#--- VERB CONJUGATIONS -----------------------------------------------------------------------------
# Verb conjugations based on a table of known verbs and rules for unknown verbs.
# Verb conjugations are useful to find the verb infinitive in the parser's lemmatizer.
# For unknown verbs, Verbs.find_lemma() and Verbs.find_lexeme() are called.
# These must be implemented in a subclass with rules for unknown verbs.

class Verbs(lazydict):

    def __init__(self, path="", format=[], default={}, language=None):
        """ A dictionary of verb infinitives, each linked to a list of conjugated forms.
            Each line in the file at the given path is one verb, with the tenses separated by a comma.
            The format defines the order of tenses (see TENSES).
            The default dictionary defines default tenses for omitted tenses.
        """
        self._path     = path
        self._language = language
        self._format   = dict((TENSES_ID[id], i) for i, id in enumerate(format))
        self._default  = default
        self._inverse  = {}

    def load(self):
        # have,,,has,,having,,,,,had,had,haven't,,,hasn't,,,,,,,hadn't,hadn't
        id = self._format[TENSES_ID[INFINITIVE]]
        for v in _read(self._path):
            v = v.split(",")
            dict.__setitem__(self, v[id], v)
            for x in (x for x in v if x):
                self._inverse[x] = v[id]

    @property
    def path(self):
        return self._path

    @property
    def language(self):
        return self._language

    @property
    def infinitives(self):
        """ Yields a dictionary of (infinitive, [inflections])-items.
        """
        if dict.__len__(self) == 0:
            self.load()
        return self

    @property
    def inflections(self):
        """ Yields a dictionary of (inflected, infinitive)-items.
        """
        if dict.__len__(self) == 0:
            self.load()
        return self._inverse

    @property
    def TENSES(self):
        """ Yields a list of tenses for this language, excluding negations.
            Each tense is a (tense, person, number, mood, aspect)-tuple.
        """
        a = set(TENSES[id] for id in self._format)
        a = a.union(set(TENSES[id] for id in self._default.keys()))
        a = a.union(set(TENSES[id] for id in self._default.values()))
        a = sorted(x[:-2] for x in a if x[-2] is False) # Exclude negation.
        return a

    def lemma(self, verb, parse=True):
        """ Returns the infinitive form of the given verb, or None.
        """
        if dict.__len__(self) == 0:
            self.load()
        if verb.lower() in self._inverse:
            return self._inverse[verb.lower()]
        if verb in self._inverse:
            return self._inverse[verb]
        if parse is True: # rule-based
            return self.find_lemma(verb)

    def lexeme(self, verb, parse=True):
        """ Returns a list of all possible inflections of the given verb.
        """
        a = []
        b = self.lemma(verb, parse=parse)
        if b in self:
            a = [x for x in self[b] if x != ""]
        elif parse is True: # rule-based
            a = self.find_lexeme(b)
        u = []; [u.append(x) for x in a if x not in u]
        return u

    def conjugate(self, verb, *args, **kwargs):
        """ Inflects the verb and returns the given tense (or None).
            For example: be
            - Verbs.conjugate("is", INFINITVE) => be
            - Verbs.conjugate("be", PRESENT, 1, SINGULAR) => I am
            - Verbs.conjugate("be", PRESENT, 1, PLURAL) => we are
            - Verbs.conjugate("be", PAST, 3, SINGULAR) => he was
            - Verbs.conjugate("be", PAST, aspect=PROGRESSIVE) => been
            - Verbs.conjugate("be", PAST, person=1, negated=True) => I wasn't
        """
        id = tense_id(*args, **kwargs)
        # Get the tense index from the format description (or a default).
        i1 = self._format.get(id)
        i2 = self._format.get(self._default.get(id))
        i3 = self._format.get(self._default.get(self._default.get(id)))
        b = self.lemma(verb, parse=kwargs.get("parse", True))
        v = []
        # Get the verb lexeme and return the requested index.
        if b in self:
            v = self[b]
            for i in (i1, i2, i3):
                if i is not None and 0 <= i < len(v) and v[i]:
                    return v[i]
        if kwargs.get("parse", True) is True: # rule-based
            v = self.find_lexeme(b)
            for i in (i1, i2, i3):
                if i is not None and 0 <= i < len(v) and v[i]:
                    return v[i]

    def tenses(self, verb, parse=True):
        """ Returns a list of possible tenses for the given inflected verb.
        """
        verb = verb.lower()
        a = set()
        b = self.lemma(verb, parse=parse)
        v = []
        if b in self:
            v = self[b]
        elif parse is True: # rule-based
            v = self.find_lexeme(b)
        # For each tense in the verb lexeme that matches the given tense,
        # 1) retrieve the tense tuple,
        # 2) retrieve the tense tuples for which that tense is a default.
        for i, tense in enumerate(v):
            if tense == verb:
                for id, index in self._format.items():
                    if i == index:
                        a.add(id)
                for id1, id2 in self._default.items():
                    if id2 in a:
                        a.add(id1)
                for id1, id2 in self._default.items():
                    if id2 in a:
                        a.add(id1)
        a = (TENSES[id][:-2] for id in a)
        a = Tenses(sorted(a))
        return a

    def find_lemma(self, verb):
        # Must be overridden in a subclass.
        # Must return the infinitive for the given conjugated (unknown) verb.
        return verb

    def find_lexeme(self, verb):
        # Must be overridden in a subclass.
        # Must return the list of conjugations for the given (unknown) verb.
        return []

class Tenses(list):

    def __contains__(self, tense):
        # t in tenses(verb) also works when t is an alias (e.g. "1sg").
        return list.__contains__(self, TENSES[tense_id(tense)][:-2])

### SENTIMENT POLARITY LEXICON #####################################################################
# A sentiment lexicon can be used to discern objective facts from subjective opinions in text.
# Each word in the lexicon has scores for:
# 1)     polarity: negative vs. positive    (-1.0 => +1.0)
# 2) subjectivity: objective vs. subjective (+0.0 => +1.0)
# 3)    intensity: modifies next word?      (x0.5 => x2.0)

# For English, adverbs are used as modifiers (e.g., "very good").
# For Dutch, adverbial adjectives are used as modifiers
# ("hopeloos voorspelbaar", "ontzettend spannend", "verschrikkelijk goed").
# Negation words (e.g., "not") reverse the polarity of the following word.

# Sentiment()(txt) returns an averaged (polarity, subjectivity)-tuple.
# Sentiment().assessments(txt) returns a list of (chunk, polarity, subjectivity, label)-tuples.

# Semantic labels are useful for fine-grained analysis, e.g.,
# negative words + positive emoticons could indicate cynicism.

# Semantic labels:
MOOD  = "mood"  # emoticons, emojis
IRONY = "irony" # sarcasm mark (!)

NOUN, VERB, ADJECTIVE, ADVERB = \
    "NN", "VB", "JJ", "RB"

RE_SYNSET = re.compile(r"^[acdnrv][-_][0-9]+$")

def avg(list):
    return sum(list) / float(len(list) or 1)

class Score(tuple):

    def __new__(self, polarity, subjectivity, assessments=[]):
        """ A (polarity, subjectivity)-tuple with an assessments property.
        """
        return tuple.__new__(self, [polarity, subjectivity])

    def __init__(self, polarity, subjectivity, assessments=[]):
        self.assessments = assessments

class Sentiment(lazydict):

    def __init__(self, path="", language=None, synset=None, confidence=None, **kwargs):
        """ A dictionary of words (adjectives) and polarity scores (positive/negative).
            The value for each word is a dictionary of part-of-speech tags.
            The value for each word POS-tag is a tuple with values for
            polarity (-1.0-1.0), subjectivity (0.0-1.0) and intensity (0.5-2.0).
        """
        self._path       = path   # XML file path.
        self._language   = None   # XML language attribute ("en", "fr", ...)
        self._confidence = None   # XML confidence attribute threshold (>=).
        self._synset     = synset # XML synset attribute ("wordnet_id", "cornetto_id", ...)
        self._synsets    = {}     # {"a-01123879": (1.0, 1.0, 1.0)}
        self.labeler     = {}     # {"dammit": "profanity"}
        self.tokenizer   = kwargs.get("tokenizer", find_tokens)
        self.negations   = kwargs.get("negations", ("no", "not", "n't", "never"))
        self.modifiers   = kwargs.get("modifiers", ("RB",))
        self.modifier    = kwargs.get("modifier" , lambda w: w.endswith("ly"))

    @property
    def path(self):
        return self._path

    @property
    def language(self):
        return self._language

    @property
    def confidence(self):
        return self._confidence

    def load(self, path=None):
        """ Loads the XML-file (with sentiment annotations) from the given path.
            By default, Sentiment.path is lazily loaded.
        """
        # <word form="great" wordnet_id="a-01123879" pos="JJ" polarity="1.0" subjectivity="1.0" intensity="1.0" />
        # <word form="damnmit" polarity="-0.75" subjectivity="1.0" label="profanity" />
        if not path:
            path = self._path
        if not os.path.exists(path):
            return
        words, synsets, labels = {}, {}, {}
        xml = cElementTree.parse(path)
        xml = xml.getroot()
        for w in xml.findall("word"):
            if self._confidence is None \
            or self._confidence <= float(w.attrib.get("confidence", 0.0)):
                w, pos, p, s, i, label, synset = (
                    w.attrib.get("form"),
                    w.attrib.get("pos"),
                    w.attrib.get("polarity", 0.0),
                    w.attrib.get("subjectivity", 0.0),
                    w.attrib.get("intensity", 1.0),
                    w.attrib.get("label"),
                    w.attrib.get(self._synset) # wordnet_id, cornetto_id, ...
                )
                psi = (float(p), float(s), float(i))
                if w:
                    words.setdefault(w, {}).setdefault(pos, []).append(psi)
                if w and label:
                    labels[w] = label
                if synset:
                    synsets.setdefault(synset, []).append(psi)
        self._language = xml.attrib.get("language", self._language)
        # Average scores of all word senses per part-of-speech tag.
        for w in words:
            words[w] = dict((pos, [avg(each) for each in zip(*psi)]) for pos, psi in words[w].items())
        # Average scores of all part-of-speech tags.
        for w, pos in list(words.items()):
            words[w][None] = [avg(each) for each in zip(*pos.values())]
        # Average scores of all synonyms per synset.
        for id, psi in synsets.items():
            synsets[id] = [avg(each) for each in zip(*psi)]
        dict.update(self, words)
        dict.update(self.labeler, labels)
        dict.update(self._synsets, synsets)

    def synset(self, id, pos=ADJECTIVE):
        """ Returns a (polarity, subjectivity)-tuple for the given synset id.
            For example, the adjective "horrible" has id 193480 in WordNet:
            Sentiment.synset(193480, pos="JJ") => (-0.6, 1.0, 1.0).
        """
        id = str(id).zfill(8)
        if not id.startswith(("n-", "v-", "a-", "r-")):
            if pos == NOUN:
                id = "n-" + id
            if pos == VERB:
                id = "v-" + id
            if pos == ADJECTIVE:
                id = "a-" + id
            if pos == ADVERB:
                id = "r-" + id
        if dict.__len__(self) == 0:
            self.load()
        try:
            return tuple(self._synsets[id])[:2]
        except KeyError: # Some WordNet id's are not zero padded.
            return tuple(self._synsets.get(re.sub(r"-0+", "-", id), (0.0, 0.0))[:2])

    def __call__(self, s, negation=True, **kwargs):
        """ Returns a (polarity, subjectivity)-tuple for the given sentence,
            with polarity between -1.0 and 1.0 and subjectivity between 0.0 and 1.0.
            The sentence can be a string, Synset, Text, Sentence, Chunk, Word, Document, Vector.
            An optional weight parameter can be given,
            as a function that takes a list of words and returns a weight.
        """
        def avg(assessments, weighted=lambda w: 1):
            s, n = 0, 0
            for words, score in assessments:
                w = weighted(words)
                s += w * score
                n += w
            return s / float(n or 1)
        # A pattern.en.wordnet.Synset.
        # Sentiment(synsets("horrible", "JJ")[0]) => (-0.6, 1.0)
        if hasattr(s, "gloss"):
            a = [(s.synonyms[0],) + self.synset(s.id, pos=s.pos) + (None,)]
        # A synset id.
        # Sentiment("a-00193480") => horrible => (-0.6, 1.0)   (English WordNet)
        # Sentiment("c_267") => verschrikkelijk => (-0.9, 1.0) (Dutch Cornetto)
        elif isinstance(s, basestring) and RE_SYNSET.match(s):
            a = [(s.synonyms[0],) + self.synset(s.id, pos=s.pos) + (None,)]
        # A string of words.
        # Sentiment("a horrible movie") => (-0.6, 1.0)
        elif isinstance(s, basestring):
            a = self.assessments(((w.lower(), None) for w in " ".join(self.tokenizer(s)).split()), negation)
        # A pattern.en.Text.
        elif hasattr(s, "sentences"):
            a = self.assessments(((w.lemma or w.string.lower(), w.pos[:2]) for w in chain(*s)), negation)
        # A pattern.en.Sentence or pattern.en.Chunk.
        elif hasattr(s, "lemmata"):
            a = self.assessments(((w.lemma or w.string.lower(), w.pos[:2]) for w in s.words), negation)
        # A pattern.en.Word.
        elif hasattr(s, "lemma"):
            a = self.assessments(((s.lemma or s.string.lower(), s.pos[:2]),), negation)
        # A pattern.vector.Document.
        # Average score = weighted average using feature weights.
        # Bag-of words is unordered: inject None between each two words
        # to stop assessments() from scanning for preceding negation & modifiers.
        elif hasattr(s, "terms"):
            a = self.assessments(chain(*(((w, None), (None, None)) for w in s)), negation)
            kwargs.setdefault("weight", lambda w: s.terms[w[0]])
        # A dict of (word, weight)-items.
        elif isinstance(s, dict):
            a = self.assessments(chain(*(((w, None), (None, None)) for w in s)), negation)
            kwargs.setdefault("weight", lambda w: s[w[0]])
        # A list of words.
        elif isinstance(s, list):
            a = self.assessments(((w, None) for w in s), negation)
        else:
            a = []
        weight = kwargs.get("weight", lambda w: 1)
        # Each "w" in "a" is a (words, polarity, subjectivity, label)-tuple.
        return Score(polarity = avg(map(lambda w: (w[0], w[1]), a), weight),
                 subjectivity = avg(map(lambda w: (w[0], w[2]), a), weight),
                  assessments = a)

    def assessments(self, words=[], negation=True):
        """ Returns a list of (chunk, polarity, subjectivity, label)-tuples for the given list of words:
            where chunk is a list of successive words: a known word optionally
            preceded by a modifier ("very good") or a negation ("not good").
        """
        a = []
        m = None # Preceding modifier (i.e., adverb or adjective).
        n = None # Preceding negation (e.g., "not beautiful").
        for w, pos in words:
            # Only assess known words, preferably by part-of-speech tag.
            # Including unknown words (polarity 0.0 and subjectivity 0.0) lowers the average.
            if w is None:
                continue
            if w in self and pos in self[w]:
                p, s, i = self[w][pos]
                # Known word not preceded by a modifier ("good").
                if m is None:
                    a.append(dict(w=[w], p=p, s=s, i=i, n=1, x=self.labeler.get(w)))
                # Known word preceded by a modifier ("really good").
                if m is not None:
                    a[-1]["w"].append(w)
                    a[-1]["p"] = max(-1.0, min(p * a[-1]["i"], +1.0))
                    a[-1]["s"] = max(-1.0, min(s * a[-1]["i"], +1.0))
                    a[-1]["i"] = i
                    a[-1]["x"] = self.labeler.get(w)
                # Known word preceded by a negation ("not really good").
                if n is not None:
                    a[-1]["w"].insert(0, n)
                    a[-1]["i"] = 1.0 / a[-1]["i"]
                    a[-1]["n"] = -1
                # Known word may be a negation.
                # Known word may be modifying the next word (i.e., it is a known adverb).
                m = None
                n = None
                if pos and pos in self.modifiers or any(map(self[w].__contains__, self.modifiers)):
                    m = (w, pos)
                if negation and w in self.negations:
                    n = w
            else:
                # Unknown word may be a negation ("not good").
                if negation and w in self.negations:
                    n = w
                # Unknown word. Retain negation across small words ("not a good").
                elif n and len(w.strip("'")) > 1:
                    n = None
                # Unknown word may be a negation preceded by a modifier ("really not good").
                if n is not None and m is not None and (pos in self.modifiers or self.modifier(m[0])):
                    a[-1]["w"].append(n)
                    a[-1]["n"] = -1
                    n = None
                # Unknown word. Retain modifier across small words ("really is a good").
                elif m and len(w) > 2:
                    m = None
                # Exclamation marks boost previous word.
                if w == "!" and len(a) > 0:
                    a[-1]["w"].append("!")
                    a[-1]["p"] = max(-1.0, min(a[-1]["p"] * 1.25, +1.0))
                # Exclamation marks in parentheses indicate sarcasm.
                if w == "(!)":
                    a.append(dict(w=[w], p=0.0, s=1.0, i=1.0, n=1, x=IRONY))
                # EMOTICONS: {("grin", +1.0): set((":-D", ":D"))}
                if w.isalpha() is False and len(w) <= 5 and w not in PUNCTUATION: # speedup
                    for (type, p), e in EMOTICONS.items():
                        if w in map(lambda e: e.lower(), e):
                            a.append(dict(w=[w], p=p, s=1.0, i=1.0, n=1, x=MOOD))
                            break
        for i in range(len(a)):
            w = a[i]["w"]
            p = a[i]["p"]
            s = a[i]["s"]
            n = a[i]["n"]
            x = a[i]["x"]
            # "not good" = slightly bad, "not bad" = slightly good.
            a[i] = (w, p * -0.5 if n < 0 else p, s, x)
        return a

    def annotate(self, word, pos=None, polarity=0.0, subjectivity=0.0, intensity=1.0, label=None):
        """ Annotates the given word with polarity, subjectivity and intensity scores,
            and optionally a semantic label (e.g., MOOD for emoticons, IRONY for "(!)").
        """
        w = self.setdefault(word, {})
        w[pos] = w[None] = (polarity, subjectivity, intensity)
        if label:
            self.labeler[word] = label

#### SPELLING CORRECTION ###########################################################################
# Based on: Peter Norvig, "How to Write a Spelling Corrector", http://norvig.com/spell-correct.html

class Spelling(lazydict):

    ALPHA = "abcdefghijklmnopqrstuvwxyz"

    def __init__(self, path=""):
        self._path = path

    def load(self):
        for x in _read(self._path):
            x = x.split()
            dict.__setitem__(self, x[0], int(x[1]))

    @property
    def path(self):
        return self._path

    @property
    def language(self):
        return self._language

    @classmethod
    def train(self, s, path="spelling.txt"):
        """ Counts the words in the given string and saves the probabilities at the given path.
            This can be used to generate a new model for the Spelling() constructor.
        """
        model = {}
        for w in re.findall("[a-z]+", s.lower()):
            model[w] = w in model and model[w] + 1 or 1
        model = ("%s %s" % (k, v) for k, v in sorted(model.items()))
        model = "\n".join(model)
        f = open(path, "w")
        f.write(model)
        f.close()

    def _edit1(self, w):
        """ Returns a set of words with edit distance 1 from the given word.
        """
        # Of all spelling errors, 80% is covered by edit distance 1.
        # Edit distance 1 = one character deleted, swapped, replaced or inserted.
        split = [(w[:i], w[i:]) for i in range(len(w) + 1)]
        delete, transpose, replace, insert = (
            [a + b[1:] for a, b in split if b],
            [a + b[1] + b[0] + b[2:] for a, b in split if len(b) > 1],
            [a + c + b[1:] for a, b in split for c in Spelling.ALPHA if b],
            [a + c + b[0:] for a, b in split for c in Spelling.ALPHA]
        )
        return set(delete + transpose + replace + insert)

    def _edit2(self, w):
        """ Returns a set of words with edit distance 2 from the given word
        """
        # Of all spelling errors, 99% is covered by edit distance 2.
        # Only keep candidates that are actually known words (20% speedup).
        return set(e2 for e1 in self._edit1(w) for e2 in self._edit1(e1) if e2 in self)

    def _known(self, words=[]):
        """ Returns the given list of words filtered by known words.
        """
        return set(w for w in words if w in self)

    def suggest(self, w):
        """ Return a list of (word, confidence) spelling corrections for the given word,
            based on the probability of known words with edit distance 1-2 from the given word.
        """
        if len(self) == 0:
            self.load()
        if len(w) == 1:
            return [(w, 1.0)] # I
        if w in PUNCTUATION:
            return [(w, 1.0)] # .?!
        if w.replace(".", "").isdigit():
            return [(w, 1.0)] # 1.5
        candidates = self._known([w]) \
                  or self._known(self._edit1(w)) \
                  or self._known(self._edit2(w)) \
                  or [w]
        candidates = [(self.get(c, 0.0), c) for c in candidates]
        s = float(sum(p for p, w in candidates) or 1)
        candidates = sorted(((p / s, w) for p, w in candidates), reverse=True)
        candidates = [(w.istitle() and x.title() or x, p) for p, x in candidates] # case-sensitive
        return candidates

#### MULTILINGUAL ##################################################################################
# The default functions in each language submodule, with an optional language parameter:
# from pattern.text import parse
# print(parse("The cat sat on the mat.", language="en"))
# print(parse("De kat zat op de mat.", language="nl"))

LANGUAGES = ["en", "es", "de", "fr", "it", "nl"]

_modules = {}
def _module(language):
    """ Returns the given language module (e.g., "en" => pattern.en).
    """
    return _modules.setdefault(language, __import__(language, globals(), {}, [], -1))

def _multilingual(function, *args, **kwargs):
    """ Returns the value from the function with the given name in the given language module.
        By default, language="en".
    """
    return getattr(_module(kwargs.pop("language", "en")), function)(*args, **kwargs)

def language(s):
    """ Returns a (language, confidence)-tuple for the given string.
    """
    s = decode_utf8(s)
    s = set(w.strip(PUNCTUATION) for w in s.replace("'", "' ").split())
    n = float(len(s) or 1)
    p = {}
    for xx in LANGUAGES:
        lexicon = _module(xx).__dict__["lexicon"]
        p[xx] = sum(1 for w in s if w in lexicon) / n
    return max(p.items(), key=lambda kv: (kv[1], int(kv[0] == "en")))
    
lang = language

def tokenize(*args, **kwargs):
    return _multilingual("tokenize", *args, **kwargs)

def parse(*args, **kwargs):
    return _multilingual("parse", *args, **kwargs)

def parsetree(*args, **kwargs):
    return _multilingual("parsetree", *args, **kwargs)

def split(*args, **kwargs):
    return _multilingual("split", *args, **kwargs)

def tag(*args, **kwargs):
    return _multilingual("tag", *args, **kwargs)

def keywords(*args, **kwargs):
    return _multilingual("keywords", *args, **kwargs)

def suggest(*args, **kwargs):
    return _multilingual("suggest", *args, **kwargs)

def sentiment(*args, **kwargs):
    return _multilingual("sentiment", *args, **kwargs)

def singularize(*args, **kwargs):
    return _multilingual("singularize", *args, **kwargs)

def pluralize(*args, **kwargs):
    return _multilingual("pluralize", *args, **kwargs)

def conjugate(*args, **kwargs):
    return _multilingual("conjugate", *args, **kwargs)

def predicative(*args, **kwargs):
    return _multilingual("predicative", *args, **kwargs)

def suggest(*args, **kwargs):
    return _multilingual("suggest", *args, **kwargs)
