Which NLP toolkit to use in JAVA? [closed]

Question

i'm working on a project that consists of a website that connects to the NCBI(National Center for Biotechnology Information) and searches for articles there. Thing is that I have to do some text mining on all the results. I'm using the JAVA language for textmining and AJAX with ICEFACES for the development of the website. What do I have : A list of articles returned from a search. Each article has an ID and an abstract. The idea is to get keywords from each abstract text. And then compare all the keywords from all abstracts and find the ones that are the most repeated. So then show in the website the related words for the search. Any ideas ? I searched a lot in the web, and I know there is Named Entity Recognition,Part Of Speech tagging, there is teh GENIA thesaurus for NER on genes and proteins, I already tried stemming ... Stop words lists, etc... I just need to know the best aproahc to resolve this problem. Thanks a lot.

Answer 1

i would recommend you use a combination of POS tagging and then string tokenizing to extract all the nouns out of each abstract.. then use some sort of dictionary/hash to count the frequency of each of these nouns and then outputting the N most prolific nouns.. combining that with some other intelligent filtering mechanisms should do reasonably well in giving you the important keywords from the abstract
for POS tagging check out the POS tagger at http://nlp.stanford.edu/software/index.shtml

However, if you are expecting a lot of multi-word terms in your corpus.. instead of extracting just nouns, you could take the most prolific n-grams for n=2 to 4

Answer 2

There's an Apache project for that... I haven't used it but, OpenNLP an open source Apache project. It's in the incubator so it maybe a bit raw.

This post from jeff's search engine cafe has a number of other suggestions.

Answer 3

This might be relevant as well: https://github.com/jdf/cue.language

It has stop words, word and ngram frequencies, ...

It's part of the software behind Wordle.

Answer 4

I ended up using the Alias`i Ling Pipe