Strategies for recognizing proper nouns in NLP

Question

I'm interested in learning more about Natural Language Processing (NLP) and am curious if there are currently any strategies for recognizing proper nouns in a text that aren't based on dictionary recognition? Also, could anyone explain or link to resources that explain the current dictionary-based methods? Who are the authoritative experts on NLP or what are the definitive resources on the subject?

Answer 1

if you have sentence such as "who is bill gates" And if you apply part of speech tagger to it. It will give answer as

"who/WP is/VBZ bill/NN gates/NNS ?/. "

U can try this online on http://cst.dk/online/pos_tagger/uk/

So you are getting what are all the nouns in this sentence. Now you can easily extract this nouns with some algorithm. I suggest to use python if you are using natural language processing. It has NLTK(Natural language toolkit) with which you can work.

Answer 2

The task of determining the proper part of speech for a word in a text is called Part of Speech Tagging. The Brill tagger, for example, uses a mixture of dictionary(vocabulary) words and contextual rules. I believe that some of the important initial dictionary words for this task are the stop words. Once you have (mostly correct) parts of speech for your words, you can start building larger structures. This industry-oriented book differentiates between recognizing noun phrases (NPs) and recognizing named entities. About textbooks: Allen's Natural Language Understanding is a good, but a bit dated, book. Foundations of Statistical Natural Language Processing is a nice introduction to statistical NLP. Speech and Language Processing is a bit more rigorous and maybe more authoritative. The Association for Computational Linguistics is a leading scientific community on computational linguistics.