Segmentation of text into tokens (words, punctuation marks, etc.). For languages using space-separated words (English. Czech, etc), the taks is relatively easy. For other languages (Chinese, Japanese, etc.) the task is much more difficult.

• description: A sample rule-based tokenizer, can use a list of prefixes which are usually followed by a dot but don't break a sentence. Distributed as a part of the Europarl tools.
• version: v6 (Jan 2012)
• author: Philipp Koehn and Josh Schroeder
• licence: free
• url: http://www.statmt.org/europarl/
• languages: in principle applicable to all languages using space-separated words; nonbreaking prefixes available for DE, EL, EN, ES, FR, IT, PT, SV.
• efficiency: NA
• reference:

@inproceedings{Koehn:2005,
author = {Philipp Koehn},
booktitle = {{Conference Proceedings: the tenth Machine Translation Summit}},
pages = {79--86},
title = {{Europarl: A Parallel Corpus for Statistical Machine Translation}},
address = {Phuket, Thailand},
year = {2005}}

• contact:

• Featurama
• Morce
• MxPost tagger
• Tree tagger
• TnT tagger
• Jan Hajič's tagger
• a number of toy tagger prototypes (students' assignments) for about ten languages

A Guide to Czech Language Tagging at UFAL http://ufal.mff.cuni.cz/czech-tagging/

* Martin Popel's lemmatizer for English
* a number of toy lemmatizers for about ten langauges (students' homeworks)
* for Czech, lemmatization is traditionally treated as a part of POS disambiguations, so almost all Czech taggers are capable of lemmatization

* Ryan McDonald's MST parser
* Rudolf Rosa's MST parser
* MALT parser
* ZPar
* Stanford parser

A Complete Guide to Czech Language Parsing http://ufal.mff.cuni.cz/czech-parsing/

* a scenario for rule-based tree transformation
* Ondřej Dušek's tools for functor assignment trained on PDT and PCEDT

Word Sense Disambiguation
Relationship Extraction
Topic Segmentation
Information Retrieval
Information Extraction
Text Sumarization
Speech Reconstruction
Question Answering
Sentiment Analysis