>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).
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.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 for interfacing with Stanford toolkit but it is somewhat brittle.
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).