Metadata-Version: 2.1
Name: cwb-ccc
Version: 0.9.6
Summary: CWB wrapper to extract concordances and collocates
Home-page: https://gitlab.cs.fau.de/pheinrich/ccc
Author: Philipp Heinrich
Author-email: philipp.heinrich@fau.de
License: UNKNOWN
Description: # Collocation and Concordance Computation #
        
        * [Introduction](#introduction)
        * [Installation](#installation)
        * [Usage](#usage)
        	* [Defining your corpus](#corpus-setup)
        	* [Extracting concordance lines](#concordancing)
        	* [Dealing with anchored queries](#anchored-queries)
        	* [Calculating collocates](#collocation-analyses)
        	* [Extracting keywords](#keyword-analyses)
        	* [Argument queries](#argument-queries)
        * [Acknowledgements](#acknowledgements)
        
        
        ## Introduction ##
        This module is a wrapper around the [IMS Open Corpus Workbench
        (CWB)](http://cwb.sourceforge.net/).  Main purpose of the module is to
        extract concordance lines, calculate keywords and collocates, and run
        queries with several anchor points.
        
        If you want to extract the results of queries with more than two
        anchor points, the module requires CWB version 3.4.16 or later.
        
        
        ## Installation ##
        You can install this module with pip from PyPI:
        
        	pip3 install cwb-ccc
        
        You can also clone the repository from
        [github](https://github.com/ausgerechnet/cwb-ccc), `cd` in the
        respective folder, and use `setup.py`:
        
        	python3 setup.py install
        
        
        ## Usage ##
        
        ### Corpus Setup
        All methods rely on the `Corpus` class, which establishes the
        connection to your CWB-indexed corpus:
        
        ```python
        from ccc import Corpus
        corpus = Corpus(
        	corpus_name="EXAMPLE_CORPUS",
        	registry_path="/path/to/your/cwb/registry/"
        )
        ```
        
        This will raise a `KeyError` if the named corpus is not in the
        specified registry.
        
        If you are using macros and wordlists, you have to store them in a
        separate folder (with subfolders `wordlists` and `macros`).  Make sure
        you specify this folder via `lib_path` when initializing the
        corpus.
        
        If you want to compare your query results according to meta data,
        set the `s_meta` parameter to the structural attribute that links your
        data base (e.g. "text_id").
        
        You can use the `cqp_bin` to point the module to a specific version of
        `cqp` (this is also helpful if `cqp` is not in your `PATH`).
        
        By default, the `cache_path` points to "/tmp/ccc-cache". Make sure
        that "/tmp/" exists and appropriate rights are granted. Otherwise,
        change the parameter when initializing the corpus (or set it to
        `None`).
        
        ### Concordancing ###
        
        Before you can display concordances, you have to run a query with the
        `corpus.query()` method, which accepts valid CQP queries such as
        
        ```python
        query = '[lemma="Angela"]? [lemma="Merkel"] [word="\\("] [lemma="CDU"] [word="\\)"]'
        corpus.query(query)
        ```
        
        The default context window is 20 tokens to the left and 20 tokens to
        the right of the query match and matchend, respectively. You can
        change this via the `context` parameter.
        
        Note that queries _may_ end on a "within" clause (`s_query`), which
        will limit the matches to regions defined by this structural
        attributes. Additionally, you can specify an `s_break` parameter,
        which will cut the context. NB: The implementation assumes that
        `s_query` regions are confined by `s_break` regions, and both of them
        are within the `s_meta` regions.
        
        Now you are set up to get the query concordance:
        
        ```
        concordance = corpus.concordance()
        ```
        
        You can access the query frequency breakdown via
        `concordance.breakdown`:
        
        | *type*                 | freq |
        |------------------------|------|
        | Angela Merkel ( CDU )  | 2253 |
        | Merkel ( CDU )         | 29   |
        | Angela Merkels ( CDU ) | 2    |
        
        All query matches and their respective `s_meta` identifiers are listed
        in `concordance.meta` (if `s_meta=None`, it will use the CQP
        identifiers of the `s_break` parameter as `s_id`):
        
        | *match* | s_id      |
        |---------|-----------|
        | 48349   | A44847086 |
        | 48856   | A44855701 |
        | 52966   | A44847097 |
        | 53395   | A44847526 |
        | ...     | ...       |
        
        You can use `concordance.lines()` to get concordance lines. This
        method returns a dictionary with the _cpos_ of the match as keys and
        the entries one concordance line each. Each concordance line is
        formatted as a `pandas.DataFrame` with the _cpos_ of each token as
        index:
        
        | *cpos* | offset | word                | anchor |
        |--------|--------|---------------------|--------|
        | 48344  | -5     | Eine                | None   |
        | 48345  | -4     | entsprechende       | None   |
        | 48346  | -3     | Steuererleichterung | None   |
        | 48347  | -2     | hat                 | None   |
        | 48348  | -1     | Kanzlerin           | None   |
        | 48349  | 0      | Angela              | None   |
        | 48350  | 0      | Merkel              | None   |
        | 48351  | 0      | (                   | None   |
        | 48352  | 0      | CDU                 | None   |
        | 48353  | 0      | )                   | None   |
        | 48354  | 1      | bisher              | None   |
        | 48355  | 2      | ausgeschlossen      | None   |
        | 48356  | 3      | .                   | None   |
        
        You can decide which and how many concordance lines you want to
        retrieve by means of the parameters `order` ("first", "last", or
        "random") and `cut_off`. You can also provide a list of `matches`
        (from `concordance.meta.index`) to get a `dict` of specific
        concordance lines.
        
        You can specify a `list` of additional p-attributes besides the
        primary word layer to show via the `p_show` parameter of
        `concordance.lines()` (these will be added as additional columns).
        
        ### Anchored Queries ###
        
        The concordancer detects anchored queries automatically. The following
        query
        
        ```python
        concordance.query(
        	'@0[lemma="Angela"]? @1[lemma="Merkel"] [word="\\("] @2[lemma="CDU"] [word="\\)"]'
        )
        ```
        
        thus returns `DataFrame`s with appropriate anchors in the anchor
        column:
        
        | *cpos* | offset | word                | anchor |
        |--------|--------|---------------------|--------|
        | 48344  | -5     | Eine                | None   |
        | 48345  | -4     | entsprechende       | None   |
        | 48346  | -3     | Steuererleichterung | None   |
        | 48347  | -2     | hat                 | None   |
        | 48348  | -1     | Kanzlerin           | None   |
        | 48349  | 0      | Angela              | 0      |
        | 48350  | 0      | Merkel              | 1      |
        | 48351  | 0      | (                   | None   |
        | 48352  | 0      | CDU                 | 2      |
        | 48353  | 0      | )                   | None   |
        | 48354  | 1      | bisher              | None   |
        | 48355  | 2      | ausgeschlossen      | None   |
        | 48356  | 3      | .                   | None   |
        
        
        ### Collocation Analyses ###
        After executing a query, you can use the `corpus.collocates()` class
        to extract collocates for a given window size (symmetric windows
        around the corpus matches):
        
        ```python
        query = '[lemma="Angela"] [lemma="Merkel"]'
        corpus.query(query, s_break='s', context=20)
        collocates = corpus.collocates()
        ```
        
        `collocates()` will create a dataframe of the context of the query
        matches. You can specify a smaller maximum window size via the `mws`
        parameter (this might be reasonable for queries with many hits). You
        will only be able to score collocates up to this parameter. Note that
        `mws` must not be larger than the `context` parameter of your initial
        query.
        
        By default, collocates are calculated on the "lemma"-layer, assuming
        that this is a valid p-attribute in the corpus. The corresponding
        parameter is `p_query` (which will fall back to "word" if the
        specified attribute is not annotated in the corpus).
        
        Using the marginal frequencies of the items in the whole corpus as a
        reference, you can directly annotate the co-occurrence counts in a
        given window:
        
        ```python
        collocates.show(window=5)
        ```
        
        The result will be a `DataFrame` with lexical items (`p_query` layer)
        as index and frequency signatures and association measures as columns:
        
        | *item*          | O11  | f2       | N         | f1    | O12   | O21      | O22       | E11         | E12          | E21          | E22          | log_likelihood | ... |
        |-----------------|------|----------|-----------|-------|-------|----------|-----------|-------------|--------------|--------------|--------------|----------------|-----|
        | die             | 1799 | 25125329 | 300917702 | 22832 | 21033 | 25123530 | 275771340 | 1906.373430 | 20925.626570 | 2.512342e+07 | 2.757714e+08 | -2.459194      | ... |
        | Bundeskanzlerin | 1491 | 8816     | 300917702 | 22832 | 21341 | 7325     | 300887545 | 0.668910    | 22831.331090 | 8.815331e+03 | 3.008861e+08 | 1822.211827    | ... |
        | .               | 1123 | 13677811 | 300917702 | 22832 | 21709 | 13676688 | 287218182 | 1037.797972 | 21794.202028 | 1.367677e+07 | 2.872181e+08 | 2.644804       | ... |
        | ,               | 814  | 17562059 | 300917702 | 22832 | 22018 | 17561245 | 283333625 | 1332.513602 | 21499.486398 | 1.756073e+07 | 2.833341e+08 | -14.204447     | ... |
        | Kanzlerin       | 648  | 17622    | 300917702 | 22832 | 22184 | 16974    | 300877896 | 1.337062    | 22830.662938 | 1.762066e+04 | 3.008772e+08 | 559.245198     | ... |
        
        For improved performance, all hapax legomena in the context are
        dropped after calculating the context size. You can change this
        behaviour via the `drop_hapaxes` parameter of `collocates.show()`.
        
        By default, the dataframe is annotated with "z_score", "t_score",
        "dice", "log_likelihood", and "mutual_information" (parameter `ams`).
        For notation and further information regarding association measures,
        see
        [collocations.de](http://www.collocations.de/AM/index.html). Available
        association measures depend on their implementation in the
        [association-measures](https://pypi.org/project/association-measures/)
        module.
        
        The dataframe is sorted by co-occurrence frequency (O11), and only the
        first 100 most frequently co-occurring collocates are retrieved. You
        can change this behaviour via the `order` and `cut_off` parameters.
        
        ### Keyword Anayses
        For keyword analyses, you will have to define a subcorpus. The natural
        way of doing so is by selecting text identifiers (on the `s_meta`
        annotations) via spreadsheets or relational databases. If you have
        collected a set of identifiers, you can create a subcorpus via the
        `corpus.subcorpus_from_ids()` method:
        
        ```python
        corpus.subcorpus_from_ids(ids)
        keywords = corpus.keywords()
        keywords.show()
        ```
        
        Just as with collocates, the result will be a `DataFrame` with lexical
        items (`p_query` layer) as index and frequency signatures and
        association measures as columns.
        
        ### Argument Queries
        Argument queries are anchored queries with additional information. (1)
        Each anchor can be modified by an offset (usually used to capture
        underspecified tokens near an anchor point). (2) Anchors can be mapped
        to external identifiers for further logical processing. (3) Anchors
        may be given a clear name:
        
        | anchor | offset | idx  | clear name |
        |--------|--------|------|------------|
        | 0      | 0      | None | None       |
        | 1      | -1     | None | None       |
        | 2      | 0      | None | None       |
        | 3      | -1     | None | None       |
        
        Furthermore, several anchor points can be combined to form regions,
        which in turn can be mapped to identifiers and be given a clear name:
        
        | start | end | idx | clear name |
        |-------|-----|-----|------------|
        | 0     | 1   | "0" | "person X" |
        | 2     | 3   | "1" | "person Y" |
        
        Example: Given the definition of anchors and regions above as well as
        suitable wordlists, the following complex query extracts corpus
        positions where there's some correlation between "person X" (the
        region from anchor 0 to anchor 1) and "person Y" (anchor 2 to 3):
        ```python
        query = (
        	"<np> []* /ap[]* [lemma = $nouns_similarity] "
        	"[]*</np> \"between\" @0:[::](<np>[pos_simple=\"D|A\"]* "
        	"([pos_simple=\"Z|P\" | lemma = $nouns_person_common | "
        	"lemma = $nouns_person_origin | lemma = $nouns_person_support | "
        	"lemma = $nouns_person_negative | "
        	"lemma = $nouns_person_profession] |/region[ner])+ "
        	"[]*</np>)+@1:[::] \"and\" @2:[::](<np>[pos_simple=\"D|A\"]* "
        	"([pos_simple=\"Z|P\" | lemma = $nouns_person_common | "
        	"lemma = $nouns_person_origin | lemma = $nouns_person_support | "
        	"lemma = $nouns_person_negative | "
        	"lemma = $nouns_person_profession] | /region[ner])+ "
        	"[]*</np>) (/region[np] | <vp>[lemma!=\"be\"]</vp> | "
        	"/region[pp] |/be_ap[])* @3:[::]"
        )
        ```
        
        NB: the set of identifiers defined in the table of anchors and in the
        table of regions, respectively, should not overlap.
        
        It is customary to store these queries in json objects (see an
        [example](tests/gold/query-example.json) in the repository). 
        
        You can use the `concordancer` to process argument queries and display
        the results:
        
        ```python
        # read the query file
        import json
        query_path = "tests/gold/query-example.json"
        with open(query_path, "rt") as f:
        	query = json.loads(f.read())
        
        # query the corpus and initialize the concordancer
        corpus.query(query['query'], context=None, s_break='tweet', match_strategy='longest')
        concordance = corpus.concordance()
        
        # show results
        concordance.show_argmin(query['anchors'], query['regions'])
        ```
        
        The `show_argmin` method returns the result as a `dict` with the
        following keys:
        
        - "nr_matches": the number of query matches in the corpus.
        - "holes": a global list of all tokens of the entities specified in
          the "idx" columns (default: lemma layer).
        - "meta": the meta ids of the concordance lines.
        - "settings": the query settings.
        - "matches": a list of concordance lines. Each concordance line
          contains:
          - "position": the corpus position of the match
          - "df": the actual concordance line as returned from
        	`Concordance().query()` (see above) converted to a `dict`
          - "holes": a mapping from the IDs specified in the anchor and region
        	tables to the tokens or token sequences, respectively (default:
        	lemma layer)
          - "full": a reconstruction of the concordance line as a sequence of
        	tokens (word layer)
        
        
        ## Acknowledgements ##
        The module relies on
        [cwb-python](https://pypi.org/project/cwb-python/), thanks to Yannick
        Versley and Jorg Asmussen for the implementation. Special thanks to
        Markus Opolka for the implementation of
        [association-measures](https://pypi.org/project/association-measures/)
        and for forcing me to write tests.
        
        This work was supported by the [Emerging Fields Initiative
        (EFI)](https://www.fau.eu/research/collaborative-research/emerging-fields-initiative/)
        of Friedrich-Alexander-Universität Erlangen-Nürnberg, project title
        [Exploring the *Fukushima
        Effect*](https://www.linguistik.phil.fau.de/projects/efe/).
        
        Further development of the package has been funded by the Deutsche
        Forschungsgemeinschaft (DFG) within the project *Reconstructing
        Arguments from Noisy Text*, grant number 377333057, as part of the
        Priority Program [Robust Argumentation
        Machines](http://www.spp-ratio.de/home/) (SPP-1999).
        
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Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
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