Stanford Log-linear Part-Of-Speech Tagger
Project Website: http://nlp.stanford.edu/software/tagger.shtml
Github Link: None
A Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads text in some language and assigns parts of speech to each word (and other token), such as noun, verb, adjective, etc., although generally computational applications use more fine-grained POS tags like ‘noun-plural’. This software is a Java implementation of the log-linear part-of-speech taggers described in these papers (if citing just one paper, cite the 2003 one):
Kristina Toutanova and Christopher D. Manning. 2000. Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000), pp. 63-70.
Kristina Toutanova, Dan Klein, Christopher Manning, and Yoram Singer. 2003. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. In Proceedings of HLT-NAACL 2003, pp. 252-259.
The tagger was originally written by Kristina Toutanova. Since that time, Dan Klein, Christopher Manning, William Morgan, Anna Rafferty, Michel Galley, and John Bauer have improved its speed, performance, usability, and support for other languages.
The system requires Java 1.8+ to be installed. Depending on whether you’re running 32 or 64 bit Java and the complexity of the tagger model, you’ll need somewhere between 60 and 200 MB of memory to run a trained tagger (i.e., you may need to give java an option like java -mx200m). Plenty of memory is needed to train a tagger. It again depends on the complexity of the model but at least 1GB is usually needed, often more.
Several downloads are available. The basic download contains two trained tagger models for English. The full download contains three trained English tagger models, an Arabic tagger model, a Chinese tagger model, a French tagger model, and a German tagger model. Both versions include the same source and other required files. The tagger can be retrained on any language, given POS-annotated training text for the language.
Part-of-speech name abbreviations: The English taggers use the Penn Treebank tag set. Here are some links to documentation of the Penn Treebank English POS tag set: 1993 Computational Linguistics article in PDF, AMALGAM page, Aoife Cahill’s list. See the included README-Models.txt in the models directory for more information about the tagsets for the other languages.