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

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.

Answer 2

Besides the dictionary-based approach, two others come to my mind:

• Pattern-based approaches (in a simple form: anything that is capitalized is a proper noun)
• Machine learning approaches (mark proper nouns in a training corpus and train a classifier)

The field is mostly called named-entity extraction and often considered a subfield of information extraction. A good starting point for the different fields of NLP is usually the according chapter in the Oxford Handbook of Computational Linguistics:

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