I'm a social scientist who is hoping to apply sense tagging / WSD as a first step in a statistical analysis of political speeches (e.g. presidential candidates 2008) and discussion. I've had some success applying POS tagging as the first step and am now investigating whether I can do better w/ sense tagging / WSD. There are, however, quite a few attractive looking algorithms on Ted Pedersen's web page, and I'm hoping someone could give me a bit of advice on which one or ones might be best for my purposes and where I could learn more about what they can do.
I'm trying to extract speaker's political concepts and the relationships between them. So, I'm interested in processing all the nouns and adjectives in a body of text. What I could ideally use is a sense tagger that would run through text and with some accuracy classify a given word into a broad cluster of meaning. For example, it should distinguish between "bank" in the sense of river bank or financial institution. SenseRelate looks like it could do this, but I'm not sure how accurate it is. I'm under the impression, perhaps incorrect, that supervised WSD would take too long if I end up trying to disambiguate a thousand or more unique words.
