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Recently in my work I came across the Backus–Naur Form (BNF), one way of describing a context-free grammar. Since then, I've been interested in learning how to deconstruct and parse not only computing lanuages but human ones as well. I realize there is a vast body of knowledge out there, but therein lies the problem. I don't know where to begin. To be clear, I'd like to understand the methodology involved in taking an English sentence and determining the subjects, actions, modifiers, etc, but I have no prior experience in this field. Where is a good place to start learning? To keep the question non-subjective, canonical textbooks or pointers to introductory wiki's are encouraged.

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This question and the submitted answers confuse me slightly because I think there is a distinction between Natural Language Processing (NLP) and Computational Linguistics. Though perhaps once related or more appropriately considered a subfield, NLP currently involves machine-learning and statistical techniques to process/translate natural language, whereas computational linguistics aims to model linguistic phenomena. I think it doesn't help that/if the two terms are sometimes used interchangeably. To clarify, do you distinguish between the two? –  Vielle Jan 31 '12 at 5:33
    
@Vielle - I don't, but only out of an (admitted) ignorance to the field. As a counterpoint to the answers by Hugo and Anthony below could you suggest a CL reference for a beginner?, –  Hooked Jan 31 '12 at 5:57
    
I am actually a beginner myself and looking to learn as well. I think part of the confusion lies in the definition of computational linguistics. It appears that some people do use it synonymously with nlp. I am using the Association for Computational Linguistics' definition mentioned at the end of the wiki article. The NLTK book others mention below sounds like a good text for applying nlp methods/tools to simplify data processing of more traditional linguistics research. –  Vielle Feb 2 '12 at 4:15
    
What I'm interested in learning more about is how to computationally model language change, origin etc. Here is a review by Simon Kirby, Natural Language from Artificial Life (2002). He does comp. ling. work on language evolution. However this review doesn't cover models of phonetics or phonology, which I would like to see. I put this topic aside for some time until I saw your question. Hopefully someone can provide more information on this path. I might ask a separate question if necessary. –  Vielle Feb 2 '12 at 4:33
    
@Hooked are you only interested in specifically computational treatments? or also in general mathematical linguistics? –  Artem Kaznatcheev Feb 3 '12 at 4:52

4 Answers 4

up vote 11 down vote accepted

The book accompanying the open-source Natural Language Toolkit, Natural Language Processing with Python --- Analyzing Text with the Natural Language Toolkit, is available as a paper book and free online, and is both a good introduction to computational linguistics and programing with the Python language.

A Language Log review of the book begins:

"This book is here to help you get your job done." I love that line (from the preface). It captures the spirit of the book. Right from the start, readers/ users get to do advanced things with large corpora, including information- rich visualizations and sophisticated theory implementation. If you've started to see that your research would benefit from some computational power, but you have limited (or no) programming experience, don't despair — install NLTK and its data sets (it's a snap), then work through this book.

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Agreed with Hugo, the NLTK book is a great free resource for basics in natural language processing and accompanying coding examples.

To add onto the list, the two textbooks assigned by my Computational Linguistics program at University Washington were:

  • "Foundations of Statistical Natural Language Processing" by Hinrich Schütze
  • "Speech and Language Processing" by Daniel Jurafsky and James H. Martin

I would definitely say they're considered two of the canonical NLP textbooks. But, since they are textbooks, they are generally more advanced and cover more topics in computational linguistics than the NLTK book.

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Of the two, which did you see as the most useful when you started? –  Hooked Jan 25 '12 at 14:31
    
Both books are prescribed textbooks at most Computational Linguistics courses in universities. Personally,I'd begun with Jurafsky and Martin. –  atlantis Jan 26 '12 at 4:56

Agreed that the Jurafsky/Martin and the NLTK book are wonderful to start with. Next up would be Finite State Morphology by Beesley/Karttunen primarily focused on xfst applications.

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  1. You can sign-up for the Stanford NLP class here. It is conducted by Jurafsky and Manning, authors of the extremely popular textbooks that the others mentioned.
  2. You can watch videos by leading researchers in the field at videolectures.net. I recommend starting with Clark's lecture and moving on to other lectures suggested in the section called "See Also".
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