Sure, but the fact is that nobody actually needs a "word-sense disambiguator", they need a search system with better accuracy, or a classifier with better accuracy or an information extraction system that turns text into facts.
Many areas in NLP are like this. You can get 92% accuracy in a few hours of work, and then you can get 93% after a week or work, and then you can write a whole PhD thesis about how you got 94% accuracy.
To a certain extent, there are approaches, such as the Support Vector Machine that are "unreasonably effective" but once you get past that, you often have to confront issues that everybody wants to sweep under the rug to make a real breakthrough.
For instance, there was that NELL paper that came out a few months ago; NELL extracted facts from text but it had no idea that "Barack Obama is the President of the United States" was true in 2010, and that "Richard Nixon is the President of the United States" was true in 1972. If you can't handle the fact that different people believe different things and that statements have expiration dates, no wonder you can only get 70% accuracy in IX
Many areas in NLP are like this. You can get 92% accuracy in a few hours of work, and then you can get 93% after a week or work, and then you can write a whole PhD thesis about how you got 94% accuracy.
To a certain extent, there are approaches, such as the Support Vector Machine that are "unreasonably effective" but once you get past that, you often have to confront issues that everybody wants to sweep under the rug to make a real breakthrough.
For instance, there was that NELL paper that came out a few months ago; NELL extracted facts from text but it had no idea that "Barack Obama is the President of the United States" was true in 2010, and that "Richard Nixon is the President of the United States" was true in 1972. If you can't handle the fact that different people believe different things and that statements have expiration dates, no wonder you can only get 70% accuracy in IX