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I am newbie to Natural Language processing. I need to extract meaningful noun and noun phrases based on their probability (eg. 75% and above) to make a auto-suggest dictionary.

I have been reading on-line posts, articles for a couple of days, but only found pieces of informations. I am thinking to use en-parser-chunking.bin model.

Could someone recommend good resources/examples that cover a use case similar to above?

Where I stand now:

Model = en-parser-chunking.bin

String line = "Tutorialspoint is the largest tutorial library.";

Tree object (output): (TOP (S (NP (NN Tutorialspoint)) (VP (VBZ is) (NP (DT the) (JJS largest) (NN tutorial) (NN library.)))))

Noun_Probability:

Tutorialspoint_0.4482373645195041 tutorial_0.6801141071099344 library._0.5625105229675064

Wendy
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1 Answers1

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I don't think this approach is good. Suggestion you can try: use the OpenNLP Chunker instead of Parser (it is lot faster). Run it in a big corpus and extract the noun phrases and its frequency. Maybe the frequency will give hints how to build your dictionary.

wcolen
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