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>Don't agree. The biggest missing piece is a statistical >parser which forms the basis for a lot of further linguistic >analysis. Conceded and agreed. This is
by jnbiche 15y ago
>Don't agree. The biggest missing piece is a statistical
>parser which forms the basis for a lot of further linguistic
>analysis.
Conceded and agreed. This is the one major gap. But I still maintain it's a remarkably complete toolkit. Plus you get to work in Python, which is a big advantage for me.
What's wrong with the Naive Bayes classifier? Did you submit a patch?
Likewise, I totally agree with you that there are faster/more accurate/more efficient implementations of many of the tools in the NLTK. If performance is a must, then you're better of prototyping in NLTK then using a specialized library. But in terms of completeness and ease of use, NLTK is very strong.
EDIT: I'm not sure why abhaga is being downvoted. There was nothing disrespectful in his response to me. Disagreement is an important part of intelligent discussion. Upvoting to counter the downvote(s).
- abhaga 15y ago> What's wrong with the Naive Bayes classifier? The problem I found is that it mixes up the binomial and the multinomial event models for the naive bayes (See http://www.cs.cmu.edu/~knigam/papers/multinomial-aaaiws98.pdf http://www.cs.cmu.edu/~knigam/papers/multinomial-aaaiws98.pd... for reference). It computes the probabilities as per the binomial event model but doesn't include the probabilities of missing events. This was my understanding from reading the source code. > Plus you get to work in Python, which is a big advantage for me. Indeed. I so wish someone would build a dependency parser on top of pfp so that I can ditch Stanford parser. I have used https://github.com/dasmith/stanford-corenlp-python https://github.com/dasmith/stanford-corenlp-python for interfacing with Stanford toolkit but it is somewhat brittle.