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I'm actually working on an information retrieval project, and I want to extract words that are of significance from a sentence. This is somewhat opposite to stopwords.

In a sentence like: He was a great astrologer and an orthodontist.

... I want to extract out words that might be of more importance. To do this, I would definitely need to filter out the stop words, but words like 'orthodontist' and 'astrologer' are far more important since they do not occur frequently. I want to be able to extract such words for my training data.

Also, my need would be satisfied if there is some API that gives you the frequency of all words that are seen in a huge enough corpus (maybe a few million sentences?) that can probably represent the distribution of words in communication in English language.

For ex: I'd input 'astrologer' and I'd get a frequency score of 0.3 (this is random, and normalized) as opposed to getting a frequency score of 0.9 for 'the'

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closed as off topic by Peter Flom, Andy W, Macro, Peter Ellis, whuber Nov 4 '12 at 20:41

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Off topic I think, unless you want to ask a question about how create your normalised score from the raw frequencies. But is en.wiktionary.org/wiki/Wiktionary:Frequency_lists#English of some help? –  Peter Ellis Oct 31 '12 at 19:52

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You can go for BNC dataset (British National Corpus) and it has documents of different genres. RASP4UIMA can be used to parse the document for syntactic information. PS: Downside with BNC is that it won't have newly coined terms.

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In this link: http://paginas.ucpel.tche.br/~loh/stoplists.zip there are 3 stoplists (portuguese, english and spanish).

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I just discovered this resource yesterday, which may have something to help you out. Aside from that, it seems like the Natural Language Toolkit would have something useful for you. They have a great python api!

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