斯坦福大学英文词性标注工具

Stanford POS Tagger, v3.8.0 - 2017-06-09 Copyright (c) 2002-2012 The Board of Trustees of The Leland Stanford Junior University. All Rights Reserved. Original tagger author: Kristina Toutanova Code contributions: Christopher Manning, Dan Klein, William Morgan, Huihsin Tseng, Anna Rafferty, John Bauer Major rewrite for version 2.0 by Michel Galley. Current release prepared by: Jason Bolton This package contains a Maximum Entropy part of speech tagger. 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 tokens), such as noun, verb, adjective, etc. 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 (POS) taggers described in: Kristina Toutanova and Christopher D. Manning. 2000. Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger. Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000), Hong Kong. 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 pages 252-259. The system requires Java 1.8+ to be installed. About 60 MB of memory is required to run a trained tagger, depending on the OS, tagging model chosen, etc. (i.e., you may need to give to java an option like java -mx120m). Plenty of memory is needed to train a tagger. It depends on the complexity of the model but at least 1GB is recommended (java -mx1g). Two trained tagger models for English are included with the tagger, along with some caseless versions, and we provide models for some other languages. The tagger can be retrained on other languages based on POS-annotated training text. If you really want to use this software under Java 1.4, look into RetroWeaver: http://retroweaver.sourceforge.net/ QUICKSTART ----------------------------------------------- The Stanford POS Tagger is designed to be used from the command line or programmatically via its API. There is a GUI interface, but it is for demonstration purposes only; most features of the tagger can only be accessed via the command line. To run the demonstration GUI you should be able to use any of the following 2 methods: 1) java -mx200m -classpath stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTaggerGUI models/wsj-0-18-left3words-distsim.tagger 2) Running the appropriate script for your operating system: stanford-postagger-gui.bat ./stanford-postagger-gui.sh To run the tagger from the command line, you can start with the provided script appropriate for you operating system: ./stanford-postagger.sh models/wsj-0-18-left3words-distsim.tagger sample-input.txt stanford-postagger models\wsj-0-18-left3words-distsim.tagger sample-input.txt The output should match what is found in sample-output.txt The tagger has three modes: tagging, training, and testing. Tagging allows you to use a pretrained model (two English models are included) to assign part of speech tags to unlabeled text. Training allows you to save a new model based on a set of tagged data that you provide. Testing allows you to see how well a tagger performs by tagging labeled data and evaluating the results against the correct tags. Many options are available for training, tagging, and testing. These options can be set using a properties file. To start, you can generate a default properties file by: java -classpath stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -genprops > myPropsFile.prop This will create the file myPropsFile.prop with descriptions of each option for the tagger and the default values for these options specified. Any properties you can specify in a properties file can be specified on the command line or vice versa. For further information, please consult the Javadocs (start with the entry for MaxentTagger, which includes a table of all options which may be set to configure the tagger and descriptions of those options). To tag a file using the pre-trained bidirectional model ======================================================= java -mx300m -classpath stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -model models/wsj-0-18-bidirectional-distsim.tagger -textFile sample-input.txt > sample-tagged.txt Tagged output will be printed to standard out, which you can redirect as above. Note that the bidirectional model is slightly more accurate but significantly slower than the left3words model. To train a simple model ======================= java -classpath stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -prop propertiesFile -model modelFile -trainFile trainingFile To test a model =============== java -classpath stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -prop propertiesFile -model modelFile -testFile testFile CONTENTS ----------------------------------------------- README.txt This file. LICENSE.txt Stanford POS Tagger is licensed under the GNU General Public License (v2+). stanford-postagger.jar stanford-postagger-YYYY-MM-DD.jar This is a JAR file containing all the Stanford classes necessary to run the Stanford POS Tagger. The two jar files are identical. You can use either the one with a version (date) indication or without, as you prefer. src A directory containing the Java 1.8 source code for the Stanford POS Tagger distribution. build.xml, Makefile Files for building the distribution (with ant and make, respectively) models A directory containing trained POS taggers; the taggers end in ".tagger" and the props file used to make the taggers end in ".props". The ".props" files cannot be directly used on your own machine as they use paths on the Stanford NLP machines, but they may serve as examples for your own properties files. Included in the full version are other English taggers, a German tagger, an Arabic tagger, and a Chinese tagger. If you chose to download the smaller version of the tagger, you have only two English taggers (left3words is faster but slightly less accurate than bidirectional-distsim) - feel free to download any other taggers you need from the POS tagger website. More information about the models can be found in the README-Models.txt file in this directory. sample-input.txt A sample text file that you can tag to demonstrate the tagger. sample-output.txt Tagged output of the tagger (using the left3words model) stanford-postagger-gui.sh stanford-postagger-gui.bat Scripts for invoking the GUI demonstration version of the tagger. stanford-postagger.sh stanford-postagger.bat Scripts for running the command-line version of the tagger. javadoc Javadocs for the distribution. In particular, look at the javadocs for the class edu.stanford.nlp.tagger.maxent.MaxentTagger. TaggerDemo.java A sample file for how to call the tagger in your own program. You should be able to compile and run it with: javac -cp stanford-postagger.jar TaggerDemo.java java -cp ".:stanford-postagger.jar" TaggerDemo models/wsj-0-18-left3words-distsim.tagger sample-input.txt (If you are on Windows, you need to replace the ":" with a ";" in the -cp argument, and should use a "\" in place of the "/" in the filename....) THANKS ----------------------------------------------- Thanks to the members of the Stanford Natural Language Processing Lab for great collaborative work on Java libraries for natural language processing. http://nlp.stanford.edu/javanlp/ CHANGES ----------------------------------------------- 2017-06-09 3.8.0 new Spanish and French UD models 2016-10-31 3.7.0 Update for compatibility, German UD model 2015-12-09 3.6.0 Updated for compatibility 2015-04-20 3.5.2 Update for