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A recurring meme in terms of frameworks is people keep bringing out tools and some of them disappear, some find niche applications, and some become mainstream.
by baltcode 16y ago
A recurring meme in terms of frameworks is people keep bringing out tools and some of them disappear, some find niche applications, and some become mainstream. Though I haven't heard of many nlp toolkits (but I'm not in that field).
I want to jump into some basic NLP, but I'd like to stick with one or two toolkits. I had heard of nltk before this, but are there any other comprehensive or sort of succesful frameworks out there one should be aware of? (Either in python or something else)
- gilesc 16y agoThe best toolkits are probably in Java: -Stanford's Tagger, Parser, and NLP Core -Apache OpenNLP -Lingpipe Many smaller components are made to be compatible with IBM UIMA (of Watson fame), so they are able to be integrated into a pipeline somewhat easily. For examples of this in biomedical TM, see http://u-compare.org/ http://u-compare.org/ . People will kill me for saying this, but truly: Python's performance isn't adequate for large-scale text mining, _especially_ if you want to do deep/full parsing. Shallow parsing as shown in this package's demo is more feasible. I personally find NLTK convoluted, but in its favor, it does have readers for a TON of corpora, which is really nice.
- devinj 16y agoMy friends in the natural language field tell me Python and NLTK are more common than Java. Then again, this is at a sort-of Python-centric university (Toronto).