What would you suggest?
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...
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