Tutorials For Natural Language Processing [closed]

Question

I recently attended a class on coursera about "Natural Language Processing" and I learnt a lot about parsing, IR and other interesting aspects like Q&A etc. though I grasped the concepts well but I did not actually get any practical knowledge of it. Can anyone suggest me good online tutorials or books for Natural Language Processing?

Thanks

Answer 1

You could read Jurafsky and Martin's Speech and Language Processing (2008 edition), which is the standard textbook in the field. It's long, and has a variety of topics, so I'd suggest reading just the chapters that really apply to your interests.

Further, the best way to learn is almost certainly to actually implement NLP algorithms from scratch. You could pick some standard tasks (language modeling, text classification, POS-tagging, NER, parsing) and implement various algorithms from the ground up (ngram models, HMMs, Naive Bayes, MaxEnt, CKY) to really understand what makes them work. It also shouldn't be too hard to find some free dataset to test your implementations on.

Finally, there are lots of tutorials out there for specific NLP algorithms that are excellent. For example, if you want to build an HMM, I suggest Jason Eisner's tutorial which also covers smoothing and unsupervised training with EM. If you want to implement Gibbs sampling for unsupervised Naive Bayes training, I suggest Philip Resnik's tutorial.

Answer 2

Aside from Jurafsky and Martin's book, Christopher D. Manning and Hinrich Schütze's Foundations of Statistical Natural Language Processing is also widely used. For IR, Manning et al. also wrote Introduction to Information Retrieval which can be read or downloaded online at their site.