Python NLTK Bayesian Classifier for word sense disambiguation - 92% accuracy

- The article does not add anything new. Using Wikipedia for word sense disambiguation has been a hot topic for some years. [1]

- The article title implies that this is somehow a spectacular finding. Doing word sense disambiguation for one word is not that interesting, and there is no comparison with existing methods to show that this is actually a high score. I suspect that it is not that spectacular, since 'Apple' is relatively easy to disambiguate using a few context words.

[1] E.g. see:

- Using Wikipedia for Automatic Word Sense Disambiguation, R. Mihalcea, 2007, for a discussion of using Wikipedia to train a word sense disambiguator.

- Integrating multiple knowledge sources to disambiguate word sense: An exemplar-based approach, H.T. Ng and H.B. Lee, 1996, provide a good overview of types of features that can be used in disambiguation. They use features that go beyond simple 'bag of word' and 'bag of n-gram' features, e.g. by using syntactical patterns.

There is a whole lot more research of course, but just to show two examples that describe far more sophisticated approaches.

It may not be the best choice of words, but capitalization plays no role in his tests as everything is transformed to lower case on line 30: http://pastebin.com/4B1xHHht

Yes, it is good, but not good enough for many applications. You're also left with the issue that one kind of "apple" is more common than the other kind of "apple" so the baseline accuracy of something that always assumes it's one kind of apple might be surprisingly good.

That said, text-to-speech is a system where it's important to do disambiguation of a particular set of words. For instead,

"I read the news today, oh boy", "read" sounds like "red"

"I read the news every day", "read" sounds like "reed"

You need to be able to disambiguate the word sense to be able to correctly read the world "read". There are maybe 20 or so very common words that are like this, so a modest amount of work in this area would be part of a good TTS system.