Mot sens de désambiguïsation dans NLTK Python

Reportez-vous http://jaganadhg.freeflux.net/blog/archive/2010/10/16/wordnet-sense-similarity-with-nltk-some-basics.html

Comme une réponse pratique à la demande du commettant, voici un python de mise en œuvre de plusieurs WSD méthodes qui retourne les sens en forme de NLTK du synset(s), https://github.com/alvations/pywsd

Il comprend

• Lesk algorithmes (comprend original Lesk, adapté Lesk et simple Lesk)
• De base algorithmes (aléatoire sens, le sens premier, le Plus Fréquent Sens)

Il peut être utilisé en tant que tel:

#!/usr/bin/env python -*- coding: utf-8 -*-

bank_sents = ['I went to the bank to deposit my money',
'The river bank was full of dead fishes']

plant_sents = ['The workers at the industrial plant were overworked',
'The plant was no longer bearing flowers']

print "======== TESTING simple_lesk ===========\n"
from lesk import simple_lesk
print "#TESTING simple_lesk() ..."
print "Context:", bank_sents[0]
answer = simple_lesk(bank_sents[0],'bank')
print "Sense:", answer
print "Definition:",answer.definition
print

print "#TESTING simple_lesk() with POS ..."
print "Context:", bank_sents[1]
answer = simple_lesk(bank_sents[1],'bank','n')
print "Sense:", answer
print "Definition:",answer.definition
print

print "#TESTING simple_lesk() with POS and stems ..."
print "Context:", plant_sents[0]
answer = simple_lesk(plant_sents[0],'plant','n', True)
print "Sense:", answer
print "Definition:",answer.definition
print

print "======== TESTING baseline ===========\n"
from baseline import random_sense, first_sense
from baseline import max_lemma_count as most_frequent_sense

print "#TESTING random_sense() ..."
print "Context:", bank_sents[0]
answer = random_sense('bank')
print "Sense:", answer
print "Definition:",answer.definition
print

print "#TESTING first_sense() ..."
print "Context:", bank_sents[0]
answer = first_sense('bank')
print "Sense:", answer
print "Definition:",answer.definition
print

print "#TESTING most_frequent_sense() ..."
print "Context:", bank_sents[0]
answer = most_frequent_sense('bank')
print "Sense:", answer
print "Definition:",answer.definition
print

[out]:

======== TESTING simple_lesk ===========

#TESTING simple_lesk() ...
Context: I went to the bank to deposit my money
Sense: Synset('depository_financial_institution.n.01')
Definition: a financial institution that accepts deposits and channels the money into lending activities

#TESTING simple_lesk() with POS ...
Context: The river bank was full of dead fishes
Sense: Synset('bank.n.01')
Definition: sloping land (especially the slope beside a body of water)

#TESTING simple_lesk() with POS and stems ...
Context: The workers at the industrial plant were overworked
Sense: Synset('plant.n.01')
Definition: buildings for carrying on industrial labor

======== TESTING baseline ===========
#TESTING random_sense() ...
Context: I went to the bank to deposit my money
Sense: Synset('deposit.v.02')
Definition: put into a bank account

#TESTING first_sense() ...
Context: I went to the bank to deposit my money
Sense: Synset('bank.n.01')
Definition: sloping land (especially the slope beside a body of water)

#TESTING most_frequent_sense() ...
Context: I went to the bank to deposit my money
Sense: Synset('bank.n.01')
Definition: sloping land (especially the slope beside a body of water)

Oui c'est possible avec l'wordnet module dans NLTK.
Les mesures de similarité utilisée dans l'outil mentionné dans ton post existe dans NLTK wordnet module de trop.