Stanford NER - v4.2.0 - 2020-11-17 ---------------------------------------------- This package provides a high-performance machine learning based named entity recognition system, including facilities to train models from supervised training data and pre-trained models for English. (c) 2002-2020. The Board of Trustees of The Leland Stanford Junior University. All Rights Reserved. Original CRF code by Jenny Finkel. Additional modules, features, internationalization, compaction, and support code by Christopher Manning, Dan Klein, Christopher Cox, Huy Nguyen Shipra Dingare, Anna Rafferty, and John Bauer. This release prepared by Jason Bolton. LICENSE The software is licensed under the full GPL v2+. Please see the file LICENCE.txt For more information, bug reports, and fixes, contact: Christopher Manning Dept of Computer Science, Gates 2A Stanford CA 94305-9020 USA java-nlp-support@lists.stanford.edu https://nlp.stanford.edu/software/CRF-NER.html CONTACT For questions about this distribution, please contact Stanford's JavaNLP group at java-nlp-user@lists.stanford.edu. We provide assistance on a best-effort basis. TUTORIAL Quickstart guidelines, primarily for end users who wish to use the included NER models, are below. For further instructions on training your own NER model, go to https://nlp.stanford.edu/software/crf-faq.html. INCLUDED SERIALIZED MODELS / TRAINING DATA The basic included serialized model is a 3 class NER tagger that can label: PERSON, ORGANIZATION, and LOCATION entities. It is included as english.all.3class.distsim.crf.ser.gz. It is trained on data from CoNLL, MUC6, MUC7, ACE, OntoNotes, and Wikipedia. Because this model is trained on both US and UK newswire, it is fairly robust across the two domains. We have also included a 4 class NER tagger trained on the CoNLL 2003 Shared Task training data that labels for PERSON, ORGANIZATION, LOCATION, and MISC. It is named english.conll.4class.distsim.crf.ser.gz . A third model is trained only on data from MUC and distinguishes between 7 different classes: english.muc.7class.distsim.crf.ser.gz. All of the serialized classifiers come in two versions, one trained to basically expected standard written English capitalization, and the other to ignore capitalization information. The case-insensitive versions of the three models available on the Stanford NER webpage. These models use a distributional similarity lexicon to improve performance (by between 1.5%-3% F-measure). The distributional similarity features make the models perform substantially better, but they require rather more memory. The distsim models are included in the release package. The nodistsim versions of the same models may be available on the Stanford NER webpage. Finally, we have models for other languages, including two German models, a Chinese model, and a Spanish model. The files for these models can be found at: http://nlp.stanford.edu/software/CRF-NER.html QUICKSTART INSTRUCTIONS This NER system requires Java 1.8 or later. Providing java is on your PATH, you should be able to run an NER GUI demonstration by just clicking. It might work to double-click on the stanford-ner.jar archive but this may well fail as the operating system does not give Java enough memory for our NER system, so it is safer to instead double click on the ner-gui.bat icon (Windows) or ner-gui.sh (Linux/Unix/MacOSX). Then, using the top option from the Classifier menu, load a CRF classifier from the classifiers directory of the distribution. You can then `either load a text file or web page from the File menu, or decide to use the default text in the window. Finally, you can now named entity tag the text by pressing the Run NER button. From a command line, you need to have java on your PATH and the stanford-ner.jar file and the lib directory in your CLASSPATH. (The way of doing this depends on your OS/shell.) The supplied ner.bat and ner.sh should work to allow you to tag a single file. For example, for Windows: ner file Or on Unix/Linux you should be able to parse the test file in the distribution directory with the command: java -mx600m -cp stanford-ner.jar:lib/* edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier classifiers/english.all.3class.distsim.crf.ser.gz -textFile sample.txt Here's an output option that will print out entities and their class to the first two columns of a tab-separated columns output file: java -mx600m -cp stanford-ner.jar:lib/* edu.stanford.nlp.ie.crf.CRFClassifier -loadClassifier classifiers/english.all.3class.distsim.crf.ser.gz -outputFormat tabbedEntities -textFile sample.txt > sample.tsv When run from a jar file, you also have the option of using a serialized classifier contained in the jar file. USING FULL STANFORD CORENLP NER FUNCTIONALITY This standalone distribution also allows access to the full NER capabilities of the Stanford CoreNLP pipeline. These capabilities can be accessed via the NERClassifierCombiner class. NERClassifierCombiner allows for multiple CRFs to be used together, and has options for recognizing numeric sequence patterns and time patterns with the rule-based NER of SUTime. Suppose one combines three CRF's CRF-1,CRF-2, and CRF-3 with the NERClassifierCombiner. When the NERClassiferCombiner runs, it will first apply the NER tags of CRF-1 to the text, then it will apply CRF-2's NER tags to any tokens not tagged by CRF-1 and so on. If the option ner.combinationMode is set to NORMAL (default), any label applied by CRF-1 cannot be applied by subsequent CRF's. For instance if CRF-1 applies the LOCATION tag, no other CRF's LOCATION tag will be used. If ner.combinationMode is set to HIGH_RECALL, this limitation will be deactivated. To use NERClassifierCombiner at the command-line, the jars in lib and stanford-ner.jar must be in the CLASSPATH. Here is an example command: java -mx2g edu.stanford.nlp.ie.NERClassifierCombiner -ner.model \ classifiers/english.conll.4class.distsim.crf.ser.gz,classifiers/english.muc.7class.distsim.crf.ser.gz \ -ner.useSUTime false -textFile sample-w-time.txt Let's break this down a bit. The flag "-ner.model" should be followed by a list of CRF's to be combined by the NERClassifierCombiner. Some serialized CRF's are provided in the classifiers directory. In this example the CRF's trained on the CONLL 4 class data and the MUC 7 class data are being combined. When the flag "-ner.useSUTime" is followed by "false", SUTime is shut off. You should note that when the "false" is omitted, the text "4 days ago" suddenly is tagged with DATE. These are the kinds of phrases SUTime can identify. NERClassifierCombiner can be run on different types of input as well. Here is an example which is run on CONLL style input: java -mx2g edu.stanford.nlp.ie.NERClassifierCombiner -ner.model \ classifiers/english.conll.4class.distsim.crf.ser.gz,classifiers/english.muc.7class.distsim.crf.ser.gz \ -map word=0,answer=1 -testFile sample-conll-file.txt It is crucial to include the "-map word=0,answer=1" , which is specifying that the input test file has the words in the first column and the answer labels in the second column. It is also possible to serialize and load an NERClassifierCombiner. This command loads the three sample crfs with combinationMode=HIGH_RECALL and SUTime=false, and dumps them to a file named test_serialized_ncc.ncc.ser.gz java -mx2g edu.stanford.nlp.ie.NERClassifierCombiner -ner.model \ classifiers/english.conll.4class.distsim.crf.ser.gz,classifiers/english.muc.7class.distsim.crf.ser.gz,\ classifiers/english.all.3class.distsim.crf.ser.gz -ner.useSUTime false \ -ner.combinationMode HIGH_RECALL -serializeTo test.serialized.ncc.ncc.ser.gz An example serialized NERClassifierCombiner with these settings is supplied in the classifiers directory. Here is an example of loading that classifier and running it on the sample CONLL data: java -mx2g edu.stanford.nlp.ie.NERClassifierCombiner -loadClassifier \ classifiers/example.serialized.ncc.ncc.ser.gz -map word=0,answer=1 \ -testFile sample-conll-file.txt For a more exhaustive description of NERClassifierCombiner go to http://nlp.stanford.edu/software/ncc-faq.html PROGRAMMATIC USE The NERDemo file illustrates a couple of ways of calling the system programatically. You should get the same results from java -cp stanford-ner.jar:lib/*:. -mx300m NERDemo classifiers/english.all.3class.distsim.crf.ser.gz sample.txt as from using CRFClassifier. For more information on API calls, look in the enclosed javadoc directory: load index.html in a browser and look first at the edu.stanford.nlp.ie.crf package and CRFClassifier class. If you wish to train your own NER systems, look also at the edu.stanford.nlp.ie package NERFeatureFactory class. SERVER VERSION The NER code may also be run as a server listening on a socket: java -mx1000m -cp stanford-ner.jar:lib/* edu.stanford.nlp.ie.NERServer 1234 You can specify which model to load with flags, either one on disk: java -mx1000m -cp stanford-ner.jar:lib/* edu.stanford.nlp.ie.NERServer -loadClassifier classifiers/all.3class.crf.ser.gz 1234 Or if you have put a model inside the jar file, as a resource under, say, models: java -mx1000m -cp stanford-ner.jar:lib/* edu.stanford.nlp.ie.NERServer -loadClassifier models/all.3class.crf.ser.gz 1234 RUNNING CLASSIFIERS FROM INSIDE A JAR FILE The software can run any serialized classifier from within a jar file by following the -loadClassifier flag by some resource available within a jar file on the CLASSPATH. An end user can make their own jar files with the desired NER models contained inside. This allows single jar file deployment. PERFORMANCE GUIDELINES Performance depends on many factors. Speed and memory use depend on hardware, operating system, and JVM. Accuracy depends on the data tested on. Nevertheless, in the belief that something is better than nothing, here are some statistics from one machine on one test set, in semi-realistic conditions (where the test data is somewhat varied). ner-eng-ie.crf-3-all2006-distsim.ser.gz (older version of ner-eng-ie.crf-3-all2008-distsim.ser.gz) Memory: 320MB (on a 32 bit machine) PERSON ORGANIZATION LOCATION 91.88 82.91 88.21 -------------------- CHANGES -------------------- 2020-11-17 4.2.0 Update for compatibility 2020-05-10 4.0.0 Update to UDv2.0 tokenization 2018-10-16 3.9.2 Update for compatibility 2018-02-27 3.9.1 KBP ner models for Chinese and Spanish 2017-06-09 3.8.0 Updated for compatibility 2016-10-31 3.7.0 Improved Chinese NER 2015-12-09 3.6.0 Updated for compatibility 2015-04-20 3.5.2 synch standalone and CoreNLP functionality 2015-01-29 3.5.1 Substantial accuracy improvements 2014-10-26 3.5.0 Upgrade to Java 1.8 2014-08-27 3.4.1 Add Spanish models 2014-06-16 3.4 Fix serialization bug 2014-01-04 3.3.1 Bugfix release 2013-11-12 3.3.0 Update for compatibility 2013-11-12 3.3.0 Update for compatibility 2013-06-19 3.2.0 Improve handling of line-by-line input 2013-04-04 1.2.8 nthreads option 2012-11-11 1.2.7 Improved English 3 class model by including data from Wikipedia, release Chinese model 2012-07-09 1.2.6 Minor bug fixes 2012-05-22 1.2.5 Fix encoding issue 2012-04-07 1.2.4 Caseless version of English models supported 2012-01-06 1.2.3 Minor bug fixes 2011-09-14 1.2.2 Improved thread safety 2011-06-19 1.2.1 Models reduced in size but on average improved in accuracy (improved distsim clusters) 2011-05-16 1.2 Normal download includes 3, 4, and 7 class models. Updated for compatibility with other software releases. 2009-01-16 1.1.1 Minor bug and usability fixes, changed API 2008-05-07 1.1 Additional feature flags, various code updates 2006-09-18 1.0 Initial release