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.
# First, you're going to need to import wordnet: from nltk.corpus import wordnet # Then, we're going to use the term "program" to find synsets like so: syns = wordnet.synsets("program") # An example of a synset: print(syns[0].name()) # Just the word: print(syns[0].lemmas()[0].name()) # Definition of that first synset: print(syns[0].definition()) # Examples of the word in use in sentences: print(syns[0].examples()) |
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)) |
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 # Let's compare the noun of "ship" and "boat:" w1 = wordnet.synset('run.v.01') # v here denotes the tag verb w2 = wordnet.synset('sprint.v.01') print(w1.wup_similarity(w2)) |
Output:
0.857142857143
w1 = wordnet.synset('ship.n.01') w2 = wordnet.synset('boat.n.01') # n denotes noun print(w1.wup_similarity(w2)) |
Output:
0.9090909090909091
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