Foundations of Statistical Natural Language Processing

Christopher D. Manning and Hinrich Schütze

The supply of probabilistic parsers on the web has increased a lot since the book was published.

Stanford Lexicalized Parser. Java factored lexicalized parser. Cross-platform, source available (Java).
Source (C++) for Eugene Charniak's probabilistic parser
Source and executables for Michael Collins' probabilistic parser. (Also, a version you can run as a daemon process by Min-Yen Kan and documentation of its adaptation to Czech by V&aacu;clav Honetschläger.)
Source and Java classes for Dan Bikel's parser, which includes an exact reimplementation of Collins' parser, and language packs for parsing certain other languages.
SSN Parsing Package. Neural net based statistical classifier based parser by James Henderson.
LoPar parser for (head-lexicalized) probabilistic context-free grammars by Helmut Schmid (for SPARC/Solaris or Intel/Linux)
More information about the DOP model can be obtained from Rens Bod's homepage
Sleepy Student parser. A probabilistic parser. Open source (not much info on how exactly it works available at its webpage).
Apple Pie Parser (Proteus Project, NYU, by Satoshi Sekine)
An implementation of the inside-outside algorithm in C can be found on Mark Johnson's software page.
A non-probabilistic parser that people often find useful is the Link Grammar parser (C source). (Link grammar is a slight variant of dependency grammar.)

Christopher Manning and Hinrich Schütze -- last modified