africanwordnet 0.0.1

Description:

africanwordnet 0.0.1

AfricanWordNet: Implementation of WordNets for African languages
This library extends OMW implemented in NLTK to add support for the following African languages.

Sepedi (nso)
Xitsonga (tsn)
Tshivenda (ven)
isiZulu (zul)
isiXhosa (xho)

[]
Requirements

Python 3
NLTK

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
>>> from nltk.corpus import wordnet as wn
>>> import africanwordnet

>>> wn.langs()
['nso', 'tsn', 'ven', 'zul', 'xho']

Setswana WordNet
>>> 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
>>> 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
>>> 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
>>> 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
>>> 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

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'}