Given a set of words categorizing a set of documents, what are some ways I could use the content of the documents to disambiguate the sense of these category words?
For example, you could have a bunch of documents tagged as being related the category "squash", which could either be about the vegetable squash or the racket-based sport squash.
My guess is that identifying category-specific words like "ball, racket, hit" in the sports sense and "cook, recipe, grow" in the vegetable sense would be helpful.
- Assuming the category-specific words would be useful, would a topic model extracting a single topic be a good approach to this? If not, what are some other approaches to identifying the keywords?
- Once the category-specific keywords are identified, how could they be used to disambiguate the original category words? One approach I've tried is using the synset of a category word that maximizes the wordnet similarity score when compared to all synsets of the category-specific words (ex: the canine sense of the category "dog" would have the highest score if a keyword was "puppy"). However, this seems inefficient and doesn't work well for words that are related but don't share a close "is a" relationship in wordnet.