Metadata-Version: 2.1
Name: africanwordnet
Version: 0.0.1
Summary: A library for African WordNet.
Home-page: https://github.com/JosephSefara/AfricanWordNet
Author: Joseph Sefara
Author-email: sefaratj@gmail.com
Maintainer: Joseph Sefara
Maintainer-email: sefaratj@gmail.com
License: MIT
Description: # AfricanWordNet: Implementation of WordNets for African languages
        
        This library extends [OMW](http://compling.hss.ntu.edu.sg/omw/) implemented in [NLTK](https://www.nltk.org/) to add support for the following African languages. 
        
        - Sepedi (nso)
        - Xitsonga (tsn)
        - Tshivenda (ven)
        - isiZulu (zul)
        - isiXhosa (xho)
        
        [![License: CC BY-NC-SA 4.0](https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc-sa/4.0/) [![GitHub release](https://img.shields.io/github/release/josephsefara/africanwordnet.svg?maxAge=3600)](https://github.com/josephsefara/africanwordnet/releases) [![Wheel](https://img.shields.io/pypi/wheel/africanwordnet.svg?maxAge=3600)](https://pypi.python.org/pypi/africanwordnet) [![python](https://img.shields.io/pypi/pyversions/africanwordnet.svg?maxAge=3600)](https://pypi.org/project/africanwordnet/) [![TotalDownloads](https://pepy.tech/badge/africanwordnet)]
        
        ## Requirements
        
        * [Python 3](https://docs.python.org/3.5/)
        * [NLTK](https://www.nltk.org/)
        ## Installation
        
        - From Pypi
          - ```pip install africanwordnet```
        - From source
          - ```pip install https://github.com/JosephSefara/AfricanWordNet.git```
        
        ## Citation Paper
        ```
        @inproceedings{sefara2020practical,
          title={Paper Title},
          author={Sefara, Tshephisho and Mokgonyane, Tumisho and Marivate, Vukosi},
          booktitle={Proceedings of the Eleventh Global Wordnet Conference},
          paages={},
          year={2020},
        }
        ```
        
        ## Usage
        
        ```python
        >>> from nltk.corpus import wordnet as wn
        >>> import africanwordnet
        
        >>> wn.langs()
        ['nso', 'tsn', 'ven', 'zul', 'xho']
        ```
        ### Setswana WordNet
        
        ```python
        >>> wn.synsets('phêpafatsa',lang=('tsn'))
        [Synset('scavenge.v.04'),
         Synset('tidy.v.01'),
         Synset('refine.v.04'),
         Synset('refine.v.03'),
         Synset('purify.v.01'),
         Synset('purge.v.04'),
         Synset('purify.v.02'),
         Synset('clean.v.08'),
         Synset('clean.v.01'),
         Synset('houseclean.v.01')]
        
        >>> wn.lemmas('phêpafatsa', lang='tsn')
        [Lemma('scavenge.v.04.phêpafatsa'),
         Lemma('tidy.v.01.phêpafatsa'),
         Lemma('refine.v.04.phêpafatsa'),
         Lemma('refine.v.03.phêpafatsa'),
         Lemma('purify.v.01.phêpafatsa'),
         Lemma('purge.v.04.phêpafatsa'),
         Lemma('purify.v.02.phêpafatsa'),
         Lemma('clean.v.08.phêpafatsa'),
         Lemma('clean.v.01.phêpafatsa'),
         Lemma('houseclean.v.01.phêpafatsa')]
        
        >>> wn.synset('purify.v.01').lemma_names('tsn')
        ['phêpafatsa']
        
        >>> lemma = wn.lemma('purify.v.01.phêpafatsa', lang='tsn')
        >>> whole_lemma.lang()
        'tsn'
        ```
        
        ### Sepedi WordNet
        
        ```python
        >>> wn.synsets('taelo',lang=('nso'))
        [Synset('call.n.12'),
         Synset('mandate.n.03'),
         Synset('command.n.01'),
         Synset('order.n.01'),
         Synset('commission.n.06'),
         Synset('commandment.n.01'),
         Synset('directive.n.01'),
         Synset('injunction.n.01')]
        
        >>> wn.lemmas('taelo', lang='nso')
        [Lemma('call.n.12.taelo'),
         Lemma('mandate.n.03.taelo'),
         Lemma('command.n.01.taelo'),
         Lemma('order.n.01.taelo'),
         Lemma('commission.n.06.taelo'),
         Lemma('commandment.n.01.taelo'),
         Lemma('directive.n.01.taelo'),
         Lemma('injunction.n.01.taelo')]
        
        >>> wn.synset('call.n.12').lemma_names('nso')
        ['taelo']
        
        >>> lemma = wn.lemma('call.n.12.taelo', lang='nso')
        >>> whole_lemma.lang()
        'nso'
        ```
        
        ### isiZulu WordNet
        
        ```python
        >>> wn.synsets('iqoqo', lang='zul')
        [Synset('whole.n.02'),
         Synset('conspectus.n.01'),
         Synset('overview.n.01'),
         Synset('sketch.n.03'),
         Synset('compilation.n.01'),
         Synset('collection.n.01'),
         Synset('team.n.02'),
         Synset('set.n.01')]
        
        >>> wn.lemmas('iqoqo', lang='zul')
        [Lemma('whole.n.02.iqoqo'),
         Lemma('conspectus.n.01.iqoqo'),
         Lemma('overview.n.01.iqoqo'),
         Lemma('sketch.n.03.iqoqo'),
         Lemma('compilation.n.01.iqoqo'),
         Lemma('collection.n.01.iqoqo'),
         Lemma('team.n.02.iqoqo'),
         Lemma('set.n.01.iqoqo')]
        
        >>> wn.synset('whole.n.02').lemma_names('zul')
        ['iqoqo']
        
        >>> whole_lemma = wn.lemma('whole.n.02.iqoqo', lang='zul')
        >>> whole_lemma.lang()
        'zul'
        ```
        
        ### isiXhosa WordNet
        
        ```python
        >>> wn.synsets('imali',lang=('xho'))
        [Synset('finance.n.03'),
         Synset('wealth.n.04'),
         Synset('capital.n.01'),
         Synset('store.n.02'),
         Synset('credit.n.02'),
         Synset('money.n.01'),
         Synset('currency.n.01'),
         Synset('purse.n.02'),
         Synset('franc.n.01'),
         Synset('cent.n.01')]
        
        >>> wn.lemmas('imali', lang='xho')
        [Lemma('finance.n.03.imali'),
         Lemma('wealth.n.04.imali'),
         Lemma('capital.n.01.imali'),
         Lemma('store.n.02.imali'),
         Lemma('credit.n.02.imali'),
         Lemma('money.n.01.imali'),
         Lemma('currency.n.01.imali'),
         Lemma('purse.n.02.imali'),
         Lemma('franc.n.01.imali'),
         Lemma('cent.n.01.imali')]
        
        >>> wn.synset('wealth.n.04').lemma_names('xho')
        ['imali']
        
        >>> lemma = wn.lemma('wealth.n.04.imali', lang='xho')
        >>> lemma.lang()
        'xho'
        ```
        
        ### Tshivenda WordNet
        
        ```python
        >>> wn.synsets('tshifanyiso',lang=('ven'))
        [Synset('picture.n.05'), 
         Synset('word_picture.n.01'), 
         Synset('portrayal.n.01')]
        
        >>> wn.lemmas('tshifanyiso', lang='ven')
        [Lemma('picture.n.05.tshifanyiso'),
         Lemma('word_picture.n.01.tshifanyiso'),
         Lemma('portrayal.n.01.tshifanyiso')]
        
        >>> wn.synset('picture.n.05').lemma_names('ven')
        ['tshifanyiso']
        
        >>> lemma = wn.lemma('picture.n.05.tshifanyiso', lang='ven')
        >>> whole_lemma.lang()
        'ven'
        ```
        
        ## Find related words
        The word **taelo** in Sepedi is related to 
        
        - tagafalo
        - molao
        - tlhalošo
        
        ```python
        words = set()
        synsets = wn.synsets('taelo',lang=('nso'))
        for synset in synsets: # synset is in english
             for hypo in synset.hyponyms():
                for lemma in hypo.lemmas("nso"):
                    words.add(lemma.name())
        print('taelo', '---', words)
        
        taelo --- {'taelo', 'tagafalo', 'molao', 'tlhalošo'}
        ```
Keywords: wordnet,python,natural language processing,nlp
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Human Machine Interfaces
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing
Classifier: Topic :: Text Processing :: Filters
Classifier: Topic :: Text Processing :: General
Classifier: Topic :: Text Processing :: Indexing
Classifier: Topic :: Text Processing :: Linguistic
Requires-Python: >=3.5.*
Description-Content-Type: text/markdown
