Getting Started in NLP

Since starting the blog, a few people have asked me how one can get started in NLP, while residing in a department lacking NLP researchers. This is a difficult question: I fell into NLP quite naturally when I was at CMU and made an easy transition to grad school at USC, both of which have awesome NLP groups. Lacking such internal support, one has to be much more ambitious to get to the point where one could do real research in the field. The obvious avenues for support are: reading books (which ones?) and papers (from where and by whom?), going to nearby conferences (which ones?) and experimentation (on what?). (New option: read and post to this blog!)

The four standard books in the field are Statistical NLP (Manning + Schutze), Speech and Language Processing (Jurafsky + Martin), Statistical Language Learning (Charniak) and Natural Language Understanding (Allen). The latter two are much older, though some people prefer Charniak to Manning + Schutze. I would probably pick up Manning + Schutze if I could only buy one. From this book, I think that skimming Chapters 1, 4, 6 and 13 should give a reasonable (but not uniformly sampled) representation of background knowledge everyone should know. Unfortunately, this misses many topics: information extraction, summarization, question answering, dialog systems, discourse, morphology, ontologies, pragmatics, semantics, sentiment analysis and textual entailment.

Finding good papers for beginners is hard. Without guidance, skimming titles and abstracts of papers published in ACL, NAACL, HLT or COLING since 2002 or 2003 should enable someone to find out what looks interesting to them. I know many advisors take this approach with new students. The ACL anthology is great for finding old papers. I'll probably post at a later date about what are the "must reads" for the areas I know best. Once you've found a few papers you like, I'd check out the respective author's web pages and see if they have any related work (best bet is probably to look at the advisor's page: often s/he will have multiple students working on similar topics). Also, advisor's often have course material and slides from tutorials: these are great places to get introductory-level material.

If you happen to get lucky (I never have) and one of the above conferences is located nearby, I'd just go. Presentations of papers (if they're good) are often better from the perspective of getting the high-level overview than the papers themselves, since papers have to be technically complete.

I'm perhaps overcommitting myself, given my promise to talk more about structured prediction, but over the next few weeks/months, I'll work on a "Getting Starting in X" series. X will likely range over the set { summarization, sequence labeling, information extraction, machine translation, language modeling }. Requests for other topics will be heard, keeping in mind I'm not an expert in many areas.