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1. Programming Language Processors in Java: Compilers and Interpreters http://www.dcs.gla.ac.uk/~daw/books/PLPJ/

2. The definitive ANTLR reference http://www.pragprog.com/titles/tpantlr

1. gives you a good introduction in how to write parsers from scratch (without lex/yacc'ish parsing frameworks), and is probably a good warm-up before the book you mention.

2. gives you an introduction to state-of-the parsing with a framework (antlr) + a some about compilation. Note: antlr also has a nice IDE for rapid developing/prototyping of parsers - antlrworks. See http://antlr.org for more info.

A very nice parsing framework for Python is dparser. It allows you to write grammars as docstrings to methods, which makes it very easy to try out things http://www.ibm.com/developerworks/linux/library/l-cpdpars.ht... http://dparser.sourceforge.net/

Definite Clause Grammars for Prolog is also worth a look (at least for reference)

Wrote a follow-up to this one (from a Computer Science perspective) at: http://amundblog.blogspot.com/2008/01/how-to-complete-your-p...

http://www.developer.com/open/article.php/998241 gives a decent overview.

My tip is to also track versions of all prod deployments so you know which one was active in production when.

"You are adamantly opposed to function/methods over 20 lines of code."

There are (at least) 2 problems with long funtions/methods: 1) They typically lead to more duplicated (and less reusable) code, e.g. repeated blocks of code between functions that could have been replaced with to-the-point short functions. A typical sign of where to extract a function is when you see a long block with a comment above it inside an even longer function, then replace the block with a function with a name inspired by the comment.

2) The methods becomes harder to test, and possibly even more important, the test code becomes harder to maintain (and untested code of some complexity usually doesn't work), e.g. what happens to your existing tests if you add some new conditions at the top of a long method?

books about machine learning applications/tools: - "Programming Collective Intelligence" - http://www.oreilly.com/catalog/9780596529321/ - "Data Mining: Practical Machine Learning Tools and Technique" - http://www.cs.waikato.ac.nz/~ml/weka/book.html

books about machine learning background/theory: - "machine learning" - http://www.cs.cmu.edu/~tom/mlbook.html - "learning and soft computing" - http://cognet.mit.edu/library/books/view?isbn=0262112558

For a simple-to-use (Python-based) machine learning tool/API check out Orange: http://magix.fri.uni-lj.si/orange/

Not sure how many.

But I know a significant amount of scientific papers are published in other languages than English, e.g. in Chinese language on Wanfang Data (www.wanfangdata.com - affiliate of Chinese Ministry of Science and Technology). This is probably the case in many other non-English languages.