Information Retrieval (Manning)

Text Compression (Bell)

Natural Language Processing (Manning)

Natural Language Understanding (Allen)

Speech and Language Processing (Jurafsky)

The Text Mining Handbook (Sanger)

Statistical Machine Translation (Koehn)

Data-Intensive Text Processing with MapReduce (Lin)

Algorithms on strings (Gusfield)

Jewels of Stringology (Crochemore)

Regular Expressions (Friedl), also: http://swtch.com/~rsc/regexp/regexp1.html and automata theory (Hopcroft)

Practical Text Mining with Perl (Bilisoly)

Natural Language Processing with Python (Bird)

Computational Linguistics (Hausser)

Syntactic structures (Chomsky)

also check out these links: http://measuringmeasures.blogspot.com/2010/01/learning-about...

http://measuringmeasures.com/blog/2010/3/12/learning-about-m...

http://www.cs.technion.ac.il/~gabr/resources/resources.html

First two lectures are great introduction to this topic and third is also related, but not necessary.

If you want to dive deeper to more advanced stuff I will recommend to look to the conditional random fields, which is kind of state of art of this field right now.

Great tutorial: http://www.cs.umass.edu/~mccallum/papers/crf-tutorial.pdf Wiki entry: http://en.wikipedia.org/wiki/Conditional_random_field

Mining The Talk: http://www.amazon.com/Mining-Talk-Unlocking-Unstructured-Inf...

Text Mining Application Programming: http://www.amazon.com/Text-Mining-Application-Programming/dp...

Introduction to Information Retrieval (available freely online): http://nlp.stanford.edu/IR-book/information-retrieval-book.h...

- Data Mining Book by Jiawei Han et al

- Managing Gigabytes by Witten et al

- Hypertext Mining book by Chakrabarti