http://www.it.iitb.ac.in/~esha/resources/firststage.pdf

http://www.inf.ed.ac.uk/teaching/courses/fnlp/Tutorials/7_WSD/tutorial.html

http://folk.uio.no/larsereb/informatikk/inf5820/NBWSD.pdf

http://nlp.stanford.edu/software/corenlp.shtml

http://nltk.googlecode.com/svn/trunk/doc/howto/wordnet.html

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General Wants

• Remove stop words and lemmatize

Naive Bayes

Wants

• Separate words into (word, part of speech, count) tuples

• Read, sentence count number of windows that words are in.

• Apply threshold to counts (use val set to pick optimal threshold; this is done later)

• Using probabilities, ignore differences in probabilities that are not stat significant across all definitions (~5%)

• Do naive bayes

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WSD Dictionary

Wants

• Read tokens
• POS tag original, carry over tag to the lemmatized
• Use tag to identify which definition to use (if the tag is noun, then use the noun definitions)
• Then look-up word
• Look up phrases large to small, removing matches in the context definitions
• Scoring will be done using this formula: (length of phrase)x(scale factor)x(frequency)
• May or may not use the example sentence along with the definitions.

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Questions

? Window size: Bayes, dict, or both?

? Stem vs. Lemmatize?

? Parsing WordNET dictionary, how?

? SenseVal?