69 lines
2.4 KiB
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69 lines
2.4 KiB
Plaintext
Stanford POS Tagger, v4.2.0 - 2020-11-17
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Copyright (c) 2002-2020 The Board of Trustees of
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The Leland Stanford Junior University. All Rights Reserved.
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This document contains (some) information about the models included in
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this release and that may be downloaded for the POS tagger website at
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http://nlp.stanford.edu/software/tagger.html . All of the models mentioned
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in this document are in the downloaded package in the same directory as this
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readme. All taggers are accompanied by the props files used to create
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them; please examine these files for more detailed information about the
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creation of the taggers.
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For English, the bidirectional taggers are slightly more accurate, but
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tag much more slowly; choose the appropriate tagger based on your
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speed/performance needs.
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English taggers
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---------------------------
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english-left3words-distsim.tagger
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Trained on WSJ sections 0-18 and extra parser training data using the
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left3words architecture and includes word shape and distributional
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similarity features. Penn tagset. UDv2.0 tokenization standard.
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english-bidirectional-distsim.tagger
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Trained on WSJ sections 0-18 using a bidirectional architecture and
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including word shape and distributional similarity features.
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Penn Treebank tagset. UDv2.0 tokenization standard.
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english-caseless-left3words-distsim.tagger
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Trained on WSJ sections 0-18 and extra parser training data using the
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left3words architecture and includes word shape and distributional
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similarity features. Penn tagset. Ignores case. UDv2.0 tokenization
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standard.
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Chinese tagger
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---------------------------
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chinese-nodistsim.tagger
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Trained on a combination of CTB7 texts from Chinese and Hong Kong
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sources.
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LDC Chinese Treebank POS tag set.
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chinese-distsim.tagger
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Trained on a combination of CTB7 texts from Chinese and Hong Kong
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sources with distributional similarity clusters.
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LDC Chinese Treebank POS tag set.
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Arabic tagger
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arabic.tagger
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Trained on the *entire* ATB p1-3.
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When trained on the train part of the ATB p1-3 split done for the 2005
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JHU Summer Workshop (Diab split), using (augmented) Bies tags, it gets
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French tagger
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---------------------------
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french-ud.tagger
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Trained on the French GSD (UDv2.2) data set
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German tagger
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---------------------------
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german-ud.tagger
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Trained on the German GSD (UDv2.2) data set
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Spanish tagger
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--------------------------
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spanish-ud.tagger
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Trained on the Spanish AnCora (UDv2.0) data set
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