Nice endeavor, but finished up as the most endeavors - unfinished. :)

That was the first book in NLP (and the only for now) that I read. I've been interested both in NLP and Haskell. In that respect it fitted, thanks!

A few points to criticize. For the frequency list one should use multisets, not dictionaries. There are a few multiset packages at Hackage. Suffix arrays are badly explained. Monads - very badly. With tagging there was an impression that it could be explained simpler.

Many things are announced but not touched. The book is not a book in fact, it's more like an article. Perhaps reconsider it in that way? But oke, hopefully you will find time to continue it as a book.

Perhaps meanwhile you can recommend some other book to continue reading on NLP?

Speech and Language Processing by Jurafsky and Martin

Foundations of Statistical Natural Language Processing by Manning and Schütze

I have to say comments like yours are not really encouraging to continue writing ;).

I'm sorry for that! All other sections were written nicely or OK, and I appreciate for what I picked up from the book. I just wanted to point out some places needed to be reworked in case you continue.

Myself being in industry, I know how hard, near to impossible it is to find time for anything extra than work and family. And a decent book requires approximately the same amount of effort as finishing PhD. Perhaps that was my frustration coming out of the projects I had to abandon. :(

Thanks for refs!

Take a look at Coursera, the NLP course by Jurafsky/Manning (authors of recommendable books) was ok; and right now there's another course starting by Collins, another state-of-art researcher in NLP.

Hi. This is my first mashup: the science outline page from wiki encoded as a JIT tree. Mashing itself is offline (written in Scheme).

There are around 700 branches of science. A bit less, perhaps 600, unique entries.

The Amazon widget is standard, and being updated according to the currently selected branch.