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https://github.com/nltk/wordnet
Stand-alone WordNet API
https://github.com/nltk/wordnet
nlp wordnet
Last synced: 4 days ago
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Stand-alone WordNet API
- Host: GitHub
- URL: https://github.com/nltk/wordnet
- Owner: nltk
- License: other
- Created: 2019-04-16T04:33:46.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-03-18T13:02:18.000Z (over 2 years ago)
- Last Synced: 2024-05-09T21:57:26.840Z (6 months ago)
- Topics: nlp, wordnet
- Language: Python
- Homepage:
- Size: 77.1 MB
- Stars: 47
- Watchers: 4
- Forks: 16
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# wordnet
[](https://travis-ci.org/nltk/wordnet)
**Notice:**
# This repository is no longer being maintained. For a standalone Python module for wordnets with a similar API, please see .
# Install
While this project is no longer maintained, you can install the last
release (0.0.23) from PyPI as follows:
```bash
pip install -U 'wn==0.0.23'
```
The version number is required because the `wn` project on PyPI is now
used by . If you're interested in
moving to the newer module, see the [migration
guide](https://wn.readthedocs.io/en/latest/guides/nltk-migration.html).
# Use
```python
>>> from wn import WordNet
>>> from wn.info import WordNetInformationContent
>>> from wn.constants import wordnet_30_dir, wordnet_33_dir
>>> wordnet = WordNet(wordnet_30_dir) # Uses WordNet v3.0 to be comparable to NLTK, by default uses v3.3
>>> wordnet.synsets('dog')
[Synset('dog.n.01'), Synset('frump.n.01'), Synset('dog.n.03'), Synset('cad.n.01'), Synset('frank.n.02'), Synset('pawl.n.01'), Synset('andiron.n.01'), Synset('chase.v.01')]
>>> wordnet.synset('dog.n.01')
Synset('dog.n.01')
>>> wordnet.synset('dog.n.01').lemma_names()
['dog', 'domestic_dog', 'Canis_familiaris']
>>> wordnet.synset('dog.n.01').lemma_names(lang='spa')
['can', 'perro']
>>> dog = wordnet.synset('dog.n.01')
>>> cat = wordnet.synset('cat.n.01')
>>> wordnet.path_similarity(dog, cat)
0.2
>>> wordnet.wup_similarity(dog, cat)
0.8571428571428571
>>> wordnet.lch_similarity(dog, cat)
2.0281482472922856
>>> wordnet_ic = WordNetInformationContent(corpus='bnc', resnik=True, add1=True)
>>> wordnet.res_similarity(dog, cat, wordnet_ic)
7.66654127408746
>>> wordnet.jcn_similarity(dog, cat, wordnet_ic)
0.3774428077151209
>>> wordnet.lin_similarity(dog, cat, wordnet_ic)
0.852667348509242
```
# With NLTK
As a comparison, this is the interface from NLTK v3.4.5
```python
>>> from nltk.corpus import wordnet
>>> from nltk.corpus import wordnet_ic
>>> wordnet.synset('dog.n.1').lemma_names()
['dog', 'domestic_dog', 'Canis_familiaris']
>>> wordnet.synset('dog.n.1').lemma_names(lang='spa')
['can', 'perro']
>>> dog = wordnet.synset('dog.n.01')
>>> cat = wordnet.synset('cat.n.01')
>>> wordnet.path_similarity(dog, cat)
0.2
>>> wordnet.wup_similarity(dog, cat)
0.8571428571428571
>>> wordnet.lch_similarity(dog, cat)
2.0281482472922856
>>> ic = wordnet_ic('ic-bnc-resnik-add1.dat')
>>> dog.res_similarity(cat, ic)
7.66654127408746
>>> dog.jcn_similarity(cat, ic)
0.3774428077151209
>>> dog.lin_similarity(cat, ic)
0.852667348509242
```