How to get synonyms/antonyms from NLTK WordNet in Python?
Last Updated :
22 Oct, 2017
WordNet is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations.
WordNet’s structure makes it a useful tool for computational linguistics and natural language processing.
WordNet superficially resembles a thesaurus, in that it groups words together based on their meanings. However, there are some important distinctions.
- First, WordNet interlinks not just word forms—strings of letters—but specific senses of words. As a result, words that are found in close proximity to one another in the network are semantically disambiguated.
- Second, WordNet labels the semantic relations among words, whereas the groupings of words in a thesaurus does not follow any explicit pattern other than meaning similarity.
from nltk.corpus import wordnet
syns = wordnet.synsets( "program" )
print (syns[ 0 ].name())
print (syns[ 0 ].lemmas()[ 0 ].name())
print (syns[ 0 ].definition())
print (syns[ 0 ].examples())
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The output will look like:
plan.n.01
plan
a series of steps to be carried out or goals to be accomplished
[‘they drew up a six-step plan’, ‘they discussed plans for a new bond issue’]
Next, how might we discern synonyms and antonyms to a word? The lemmas will be synonyms, and then you can use .antonyms to find the antonyms to the lemmas. As such, we can populate some lists like:
import nltk
from nltk.corpus import wordnet
synonyms = []
antonyms = []
for syn in wordnet.synsets( "good" ):
for l in syn.lemmas():
synonyms.append(l.name())
if l.antonyms():
antonyms.append(l.antonyms()[ 0 ].name())
print ( set (synonyms))
print ( set (antonyms))
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The output will be two sets of synonyms and antonyms
{‘beneficial’, ‘just’, ‘upright’, ‘thoroughly’, ‘in_force’, ‘well’, ‘skilful’, ‘skillful’, ‘sound’, ‘unspoiled’, ‘expert’, ‘proficient’, ‘in_effect’, ‘honorable’, ‘adept’, ‘secure’, ‘commodity’, ‘estimable’, ‘soundly’, ‘right’, ‘respectable’, ‘good’, ‘serious’, ‘ripe’, ‘salutary’, ‘dear’, ‘practiced’, ‘goodness’, ‘safe’, ‘effective’, ‘unspoilt’, ‘dependable’, ‘undecomposed’, ‘honest’, ‘full’, ‘near’, ‘trade_good’} {‘evil’, ‘evilness’, ‘bad’, ‘badness’, ‘ill’}
Now , let’s compare the similarity index of any two words
import nltk
from nltk.corpus import wordnet
w1 = wordnet.synset( 'run.v.01' )
w2 = wordnet.synset( 'sprint.v.01' )
print (w1.wup_similarity(w2))
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Output:
0.857142857143
w1 = wordnet.synset( 'ship.n.01' )
w2 = wordnet.synset( 'boat.n.01' )
print (w1.wup_similarity(w2))
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Output:
0.9090909090909091
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