NEW!!! (03.04.14): WSD by maximizing sense similarity with various similarity measures.

Python Implementations of Word Sense Disambiguation (WSD) technologies:

• Lesk algorithms
  • Original Lesk (Lesk, 1986)
  • Adapted/Extended Lesk (Banerjee and Pederson, 2002/2003)
  • Simple Lesk (with definition, example(s) and hyper+hyponyms)
  • Cosine Lesk (use cosines to calculate overlaps instead of using raw counts)
• Maximizing Similarity (see also, http://goo.gl/MG8ZpE)
  • Path similarity (Wu-Palmer, 1994; Leacock and Chodorow, 1998)
  • Information Content (Resnik, 1995; Jiang and Corath, 1997; Lin, 1998)
• Vector Space Models (in progress)
  • LSI/LSA
  • Topic Models, LDA (Li et al. 2012)
  • NMF
• Baselines
  • Random sense
  • First NLTK sense
  • Highest lemma counts

(NOTE: requires NLTK, goto http://nltk.org/install.html. Also, note that as of 30.10.13, NLTK has changed Synset object properties to methods, see http://goo.gl/hO79KO)

15.04.14:

• Changed Synset properties to methods adhering to new NLTK version (see see http://goo.gl/hO79KO)
• Added N-best results, library now outputs ranked list of synsets (possible with normalized scores too), see test_wsd.py

$ cd pywsd/
$ ls
baseline.py   cosine.py   lesk.py   README.md   test_wsd.py
$ python
>>> from lesk import simple_lesk
>>> sent = 'I went to the bank to deposit my money'
>>> ambiguous = 'bank'
>>> answer = simple_lesk(sent, ambiguous)
>>> print answer
Synset('depository_financial_institution.n.01')
>>> print answer.definition()
a financial institution that accepts deposits and channels the money into lending activities

• Michael Lesk. 1986. Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In Proceedings of the 5th annual international conference on Systems documentation (SIGDOC '86), Virginia DeBuys (Ed.). ACM, New York, NY, USA, 24-26. DOI=10.1145/318723.318728 http://doi.acm.org/10.1145/318723.318728

• Satanjeev Banerjee and Ted Pedersen. 2002. An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet. In Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing (CICLing '02), Alexander F. Gelbukh (Ed.). Springer-Verlag, London, UK, UK, 136-145.

• Satanjeev Banerjee and Ted Pedersen. 2003. Extended gloss overlaps as a measure of semantic relatedness. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pages 805–810, Acapulco.

• Jay J. Jiang and David W. Conrath. 1997. Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of International Conference on Research in Computational Linguistics, Taiwan.

• Claudia Leacock and Martin Chodorow. 1998. Combining local context and WordNet similarity for word sense identification. In Fellbaum 1998, pp. 265–283.

• Dekang Lin. 1998. An information-theoretic definition of similarity. In Proceedings of the 15th International Conference on Machine Learning, Madison, WI.

• Linlin Li, Benjamin Roth and Caroline Sporleder. 2010. Topic Models for Word Sense Disambiguation and Token-based Idiom Detection. The 48th Annual Meeting of the Association for Computational Linguistics (ACL). Uppsala, Sweden.

• Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural Language Processing with Python (1st ed.). O'Reilly Media, Inc..