stanford-postagger-full-2016-10-31/models/README-Models.txt
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Stanford POS Tagger, v3.7.0 - 2016-10-31 Copyright (c) 2002-2012 The Board of Trustees of The Leland Stanford Junior University. All Rights Reserved. This document contains (some) information about the models included in this release and that may be downloaded for the POS tagger website at http://nlp.stanford.edu/software/tagger.shtml . If you have downloaded the full tagger, all of the models mentioned in this document are in the downloaded package in the same directory as this readme. Otherwise, included in the download are two English taggers, and the other taggers may be downloaded from the website. All taggers are accompanied by the props files used to create them; please examine these files for more detailed information about the creation of the taggers. For English, the bidirectional taggers are slightly more accurate, but tag much more slowly; choose the appropriate tagger based on your speed/performance needs. English taggers --------------------------- wsj-0-18-bidirectional-distsim.tagger Trained on WSJ sections 0-18 using a bidirectional architecture and including word shape and distributional similarity features. Penn Treebank tagset. Performance: 97.28% correct on WSJ 19-21 (90.46% correct on unknown words) wsj-0-18-left3words.tagger Trained on WSJ sections 0-18 using the left3words architecture and includes word shape features. Penn tagset. Performance: 96.97% correct on WSJ 19-21 (88.85% correct on unknown words) wsj-0-18-left3words-distsim.tagger Trained on WSJ sections 0-18 using the left3words architecture and includes word shape and distributional similarity features. Penn tagset. Performance: 97.01% correct on WSJ 19-21 (89.81% correct on unknown words) english-left3words-distsim.tagger Trained on WSJ sections 0-18 and extra parser training data using the left3words architecture and includes word shape and distributional similarity features. Penn tagset. english-bidirectional-distsim.tagger Trained on WSJ sections 0-18 using a bidirectional architecture and including word shape and distributional similarity features. Penn Treebank tagset. wsj-0-18-caseless-left3words-distsim.tagger Trained on WSJ sections 0-18 left3words architecture and includes word shape and distributional similarity features. Penn tagset. Ignores case. english-caseless-left3words-distsim.tagger Trained on WSJ sections 0-18 and extra parser training data using the left3words architecture and includes word shape and distributional similarity features. Penn tagset. Ignores case. Chinese tagger --------------------------- chinese-nodistsim.tagger Trained on a combination of CTB7 texts from Chinese and Hong Kong sources. LDC Chinese Treebank POS tag set. Performance: 93.46% on a combination of Chinese and Hong Kong texts (79.40% on unknown words) chinese-distsim.tagger Trained on a combination of CTB7 texts from Chinese and Hong Kong sources with distributional similarity clusters. LDC Chinese Treebank POS tag set. Performance: 93.99% on a combination of Chinese and Hong Kong texts (84.60% on unknown words) Arabic tagger --------------------------- arabic.tagger Trained on the *entire* ATB p1-3. When trained on the train part of the ATB p1-3 split done for the 2005 JHU Summer Workshop (Diab split), using (augmented) Bies tags, it gets the following performance: 96.26% on test portion according to Diab split (80.14% on unknown words) French tagger --------------------------- french.tagger Trained on the French treebank. German tagger --------------------------- german-hgc.tagger Trained on the first 80% of the Negra corpus, which uses the STTS tagset. The Stuttgart-Tübingen Tagset (STTS) is a set of 54 tags for annotating German text corpora with part-of-speech labels, which was jointly developed by the Institut für maschinelle Sprachverarbeitung of the University of Stuttgart and the Seminar für Sprachwissenschaft of the University of Tübingen. See: http://www.ims.uni-stuttgart.de/projekte/CQPDemos/Bundestag/help-tagset.html This model uses features from the distributional similarity clusters built over the HGC (Huge German Corpus). Performance: 96.90% on the first half of the remaining 20% of the Negra corpus (dev set) (90.33% on unknown words) german-fast.tagger Lacks distributional similarity features, but is several times faster than the other alternatives. Performance: 96.61% overall / 86.72% unknown. german-ud.tagger This is a model that produces Universal Dependencies POS tags. |