3 ms·
This is an interesting exercise in building a very specific word disambiguator ('apple' the company vs 'apple' the fruit). It is a testmanet to NLTK that this
by tkahnoski 16y ago
This is an interesting exercise in building a very specific word disambiguator ('apple' the company vs 'apple' the fruit).
It is a testmanet to NLTK that this can be accomplished in less than 100 lines.
- danieldk 16y agoMaybe apart from stemming), it's not hard to implement this in ~100 lines without NLTK.: - In naive Bayes classification, model parameters can usually be estimated using relative frequencies in the training data. - WordPunctTokenizer is a very simple tokenizer that makes anything matching \w+ and [^\w\s]+ a separate token. - Extracting Bigrams from a list of tokens is trivial. Of course, using NLTK will be very helpful in many situations, but this is hardly a testament to NLTK.