nlp - Finding related words (specifically physical objects) to a specific word

Question

I am trying to find words (specifically physical objects) related to a single word. For example:

Tennis: tennis racket, tennis ball, tennis shoe

Snooker: snooker cue, snooker ball, chalk

Chess: chessboard, chess piece

Bookcase: book

I have tried to use WordNet, specifically the meronym semantic relationship; however, this method is not consistent as the results below show:

Tennis: serve, volley, foot-fault, set point, return, advantage

Snooker: nothing

Chess: chess move, checkerboard (whose own meronym relationships shows ‘square’ & 'diagonal')

Bookcase: shelve

Weighting of terms will eventually be required, but that is not really a concern now.

Anyone have any suggestions on how to do this?

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Just an update: Ended up using a mixture of both Jeff's and StompChicken's answers.

The quality of information retrieved from Wikipedia is excellent, specifically how (unsurprisingly) there is so much relevant information (in comparison to some corpora where terms such as 'blog' and 'ipod' do not exist).

The range of results from Wikipedia is the best part. The software is able to match terms such as (lists cut for brevity):

• golf: [ball, iron, tee, bag, club]
• photography: [camera, film, photograph, art, image]
• fishing: [fish, net, hook, trap, bait, lure, rod]

The biggest problem is classifying certain words as physical artefacts; default WordNet is not a reliable resource as many terms (such as 'ipod', and even 'trampolining') do not exist in it.

Answer 1

I think what you are asking for is a source of semantic relationships between concepts. For that, I can think of a number of ways to go:

1. Semantic similarity algorithms. These algorithms usually perform a tree walk over the relationships in Wordnet to come up with a real-valued score of how related two terms are. These will be limited by how well WordNet models the concepts that you are interested in. WordNet::Similarity (written in Perl) is pretty good.
2. Try using OpenCyc as a knowledge base. OpenCyc is a open-source version of Cyc, a very large knowledge base of 'real-world' facts. It should have a much richer set of sematic realtionships than WordNet does. However, I have never used OpenCyc so I can't speak to how complete it is, or how easy it is to use.
3. n-gram frequency analysis. As mentioned by Jeff Moser. A data-driven approach that can 'discover' relationships from large amounts of data, but can often produce noisy results.
4. Latent Semantic Analysis. A data-driven approach similar to n-gram frequency analysis that finds sets of semantically related words.

[...]

Judging by what you say you want to do, I think the last two options are more likely to be successful. If the relationships are not in Wordnet then semantic similarity won't work and OpenCyc doesn't seem to know much about snooker other than the fact that it exists.

I think a combination of both n-grams and LSA (or something like it) would be a good idea. N-gram frequencies will find concepts tightly bound to your target concept (e.g. tennis ball) and LSA would find related concepts mentioned in the same sentence/document (e.g. net, serve). Also, if you are only interested in nouns, filtering your output to contain only nouns or noun phrases (by using a part-of-speech tagger) might improve results.