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Vivek SrikumarStanford NLP
Vivek Srikumar
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Stanford NLP
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‎CONTRIBUTING.md

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@@ -10,7 +10,7 @@ In order for us to continue to be able to dual-license Stanford CoreNLP, we need
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Therefore, we can accept contributions on any of the following terms:
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* If your contribution is a bug fix of 6 lines or less of new code, we will accept it on the basis that both you and us regard the contribution as de minimis, and not requiring further hassle.
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* You can declare that the contribution is in the public domain (in your commit message or pull request).
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* You can make your contribution available under a non-restrictive open source license, such as the Revised (or 3-clause) BSD license, with appropriate licensing information included with the submitted code.
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* You can sign and return to us a contributor license agreement (CLA), explicitly licensing us to be able to use the code. You can find these agreements at http://nlp.stanford.edu/software/CLA/ . You can send them to us or contact us at: [email protected] .
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* You can make your contribution available under a non-restrictive open source licensing, such as the Revised (or 3-clause) BSD license, with appropriate licensing information included with the submitted code.
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* You can sign and return to us a contributor license agreement, explicitly licensing us to be able to use the code. Contact us at: [email protected] .
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You should do development against our master branch. The project's source code is in utf-8 character encoding. You should make sure that all unit tests still pass. (In general, you will not be able to run our integration tests, since they rely on resources in our filesystem.)
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You should do development against our master branch. You should make sure that all unit tests still pass. (In general, you will not be able to run our integration tests, since they rely on resources in our filesystem.)

‎data/edu/stanford/nlp/classify/iris.gold

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5 Iris-setosa Iris-setosa 0.996 0.996
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4.6 Iris-setosa Iris-setosa 0.999 0.999
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5.1 Iris-setosa Iris-setosa 0.994 0.994
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4.9 Iris-setosa Iris-setosa 0.991 0.991
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5.4 Iris-setosa Iris-setosa 0.998 0.998
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4.4 Iris-setosa Iris-setosa 0.994 0.994
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5.3 Iris-setosa Iris-setosa 0.998 0.998
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6.1 Iris-versicolor Iris-versicolor 0.887 0.887
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6 Iris-versicolor Iris-versicolor 0.942 0.942
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5.5 Iris-versicolor Iris-versicolor 0.903 0.903
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6.5 Iris-versicolor Iris-versicolor 0.929 0.929
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6.8 Iris-versicolor Iris-versicolor 0.957 0.957
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6.2 Iris-versicolor Iris-versicolor 0.986 0.986
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6.7 Iris-virginica Iris-virginica 0.970 0.970
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6.4 Iris-virginica Iris-virginica 0.898 0.898
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5.7 Iris-virginica Iris-virginica 0.992 0.992
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6.7 Iris-virginica Iris-virginica 0.969 0.969
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6.8 Iris-virginica Iris-virginica 0.996 0.996
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7.7 Iris-virginica Iris-virginica 0.990 0.990
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7.3 Iris-virginica Iris-virginica 0.977 0.977
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5 Iris-setosa Iris-setosa 0.995615365125735
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4.6 Iris-setosa Iris-setosa 0.9994804135630505
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5.1 Iris-setosa Iris-setosa 0.9937095680980086
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4.9 Iris-setosa Iris-setosa 0.9905109629700247
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5.4 Iris-setosa Iris-setosa 0.9982151488134486
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4.4 Iris-setosa Iris-setosa 0.9944214428148407
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5.3 Iris-setosa Iris-setosa 0.9984497925740373
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6.1 Iris-versicolor Iris-versicolor 0.8873152482428373
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6 Iris-versicolor Iris-versicolor 0.9424246013278404
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5.5 Iris-versicolor Iris-versicolor 0.9030026595536319
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6.5 Iris-versicolor Iris-versicolor 0.928816167001929
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6.8 Iris-versicolor Iris-versicolor 0.9569376555329442
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6.2 Iris-versicolor Iris-versicolor 0.9857141927233324
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6.7 Iris-virginica Iris-virginica 0.9698639532763317
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6.4 Iris-virginica Iris-virginica 0.8982390073296296
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5.7 Iris-virginica Iris-virginica 0.9920401400173403
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6.7 Iris-virginica Iris-virginica 0.968576539063806
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6.8 Iris-virginica Iris-virginica 0.9957320369272686
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7.7 Iris-virginica Iris-virginica 0.9900526044768513
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7.3 Iris-virginica Iris-virginica 0.9766204287594443

‎data/edu/stanford/nlp/classify/iris.l1reg.gold

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5 Iris-setosa Iris-setosa 0.992 0.992
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4.6 Iris-setosa Iris-setosa 0.999 0.999
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5.1 Iris-setosa Iris-setosa 0.989 0.989
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4.9 Iris-setosa Iris-setosa 0.984 0.984
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5.4 Iris-setosa Iris-setosa 0.996 0.996
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4.4 Iris-setosa Iris-setosa 0.991 0.991
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5.3 Iris-setosa Iris-setosa 0.997 0.997
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6.1 Iris-versicolor Iris-versicolor 0.847 0.847
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6 Iris-versicolor Iris-versicolor 0.931 0.931
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5.5 Iris-versicolor Iris-versicolor 0.798 0.798
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6.5 Iris-versicolor Iris-versicolor 0.873 0.873
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6.8 Iris-versicolor Iris-versicolor 0.914 0.914
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6.2 Iris-versicolor Iris-versicolor 0.969 0.969
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6.7 Iris-virginica Iris-virginica 0.951 0.951
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6.4 Iris-virginica Iris-virginica 0.833 0.833
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5.7 Iris-virginica Iris-virginica 0.986 0.986
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6.7 Iris-virginica Iris-virginica 0.928 0.928
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6.8 Iris-virginica Iris-virginica 0.987 0.987
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7.7 Iris-virginica Iris-virginica 0.981 0.981
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7.3 Iris-virginica Iris-virginica 0.956 0.956
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5 Iris-setosa Iris-setosa 0.9919247137755053
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4.6 Iris-setosa Iris-setosa 0.9988153870786971
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5.1 Iris-setosa Iris-setosa 0.9893228231715544
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4.9 Iris-setosa Iris-setosa 0.9835318845429561
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5.4 Iris-setosa Iris-setosa 0.9960427411240634
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4.4 Iris-setosa Iris-setosa 0.9910859075339642
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5.3 Iris-setosa Iris-setosa 0.9965862883009643
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6.1 Iris-versicolor Iris-versicolor 0.8468902641192759
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6 Iris-versicolor Iris-versicolor 0.9307517829994151
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5.5 Iris-versicolor Iris-versicolor 0.7982164305911292
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6.5 Iris-versicolor Iris-versicolor 0.873020490772672
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6.8 Iris-versicolor Iris-versicolor 0.9142958840729118
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6.2 Iris-versicolor Iris-versicolor 0.9691329948474605
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6.7 Iris-virginica Iris-virginica 0.9514065325627161
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6.4 Iris-virginica Iris-virginica 0.8326970803989662
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5.7 Iris-virginica Iris-virginica 0.9861478471561218
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6.7 Iris-virginica Iris-virginica 0.9281387678310443
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6.8 Iris-virginica Iris-virginica 0.9869791941203433
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7.7 Iris-virginica Iris-virginica 0.980694494307154
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7.3 Iris-virginica Iris-virginica 0.9555631398239129

‎data/edu/stanford/nlp/dcoref/expected.txt

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CONLL EVAL SUMMARY (Before COREF)
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Identification of Mentions: Recall: (12407 / 14291) 86.81% Precision: (12407 / 34999) 35.44% F1: 50.34%
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Identification of Mentions: Recall: (12407 / 14291) 86.81% Precision: (12407 / 34999) 35.44% F1: 50.34%
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CONLL EVAL SUMMARY (After COREF)
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METRIC muc:Coreference: Recall: (6256 / 10539) 59.36% Precision: (6256 / 10078) 62.07% F1: 60.68%
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METRIC bcub:Coreference: Recall: (12462.33 / 18385) 67.78% Precision: (13629.92 / 18385) 74.13% F1: 70.81%
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METRIC ceafm:Coreference: Recall: (10928 / 18385) 59.43% Precision: (10928 / 18385) 59.43% F1: 59.43%
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METRIC ceafe:Coreference: Recall: (3832.95 / 7846) 48.85% Precision: (3832.95 / 8307) 46.14% F1: 47.45%
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METRIC blanc:Coreference links: Recall: (25245 / 54427) 46.38% Precision: (25245 / 40608) 62.16% F1: 53.12%
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Non-coreference links: Recall: (932068 / 947431) 98.37% Precision: (932068 / 961250) 96.96% F1: 97.66%
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BLANC: Recall: (0.72 / 1) 72.38% Precision: (0.8 / 1) 79.56% F1: 75.39%
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METRIC muc:Coreference: Recall: (6260 / 10539) 59.39% Precision: (6260 / 10027) 62.43% F1: 60.87%
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METRIC bcub:Coreference: Recall: (12379.37 / 18298) 67.65% Precision: (13598.84 / 18298) 74.31% F1: 70.83%
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METRIC ceafm:Coreference: Recall: (10894 / 18298) 59.53% Precision: (10894 / 18298) 59.53% F1: 59.53%
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METRIC ceafe:Coreference: Recall: (3811.5 / 7759) 49.12% Precision: (3811.5 / 8271) 46.08% F1: 47.55%
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METRIC blanc:Coreference links: Recall: (25257 / 54427) 46.4% Precision: (25257 / 40544) 62.29% F1: 53.18%
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Non-coreference links: Recall: (922975 / 938262) 98.37% Precision: (922975 / 952145) 96.93% F1: 97.64%
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BLANC: Recall: (0.72 / 1) 72.38% Precision: (0.8 / 1) 79.61% F1: 75.41%
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Final conll score ((muc+bcub+ceafe)/3) = 59.65
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Final conll score ((muc+bcub+ceafe)/3) = 59.75
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Final score (pairwise) Precision = 0.57
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done

‎itest/src/edu/stanford/nlp/pipeline/DeterministicCorefAnnotatorITest.java

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import edu.stanford.nlp.ling.CoreAnnotations;
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import edu.stanford.nlp.ling.CoreLabel;
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import edu.stanford.nlp.dcoref.CorefCoreAnnotations;
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import edu.stanford.nlp.dcoref.CorefCoreAnnotations;
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import edu.stanford.nlp.util.CoreMap;
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public class DeterministicCorefAnnotatorITest extends TestCase {
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private static AnnotationPipeline pipeline;
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@Override
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public void setUp() throws Exception {
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synchronized(DeterministicCorefAnnotatorITest.class) {
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pipeline = new AnnotationPipeline();
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DeterministicCorefAnnotatorITest itest = new DeterministicCorefAnnotatorITest();
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itest.testDeterministicCorefAnnotator();
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}
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}

‎scripts/makeSerialized.csh

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# This now works
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( echo "Running xinhuaFactored from serialized (check without specifying -tLPP) on $host -server" ; time java -server -mx1800m edu.stanford.nlp.parser.lexparser.LexicalizedParser -evals "factDA,tsv" -maxLength 40 -loadFromSerializedFile xinhuaFactored.ser.gz -test $ctb 001-025 ) >>& ./serializedParsers.log
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( echo "Running chinesePCFG (simplified for use in the RNN parser) on $host -server" ; time java -server -mx4g edu.stanford.nlp.parser.lexparser.LexicalizedParser -evals "factDA,tsv" -tLPP edu.stanford.nlp.parser.lexparser.ChineseTreebankParserParams -chineseFactored -PCFG -hMarkov 1 -nomarkNPconj -compactGrammar 0 -saveToSerializedFile chinesePCFG.simple.ser.gz -maxLength 40 -train $ctb7train -test $ctb7test ) >>& ./serializedParsers.log
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( echo "Running chinesePCFG (simplified for use in the RNN parser) on $host -server" ; time java -server -mx4g edu.stanford.nlp.parser.lexparser.LexicalizedParser -evals "factDA,tsv" -tLPP edu.stanford.nlp.parser.lexparser.ChineseTreebankParserParams -chineseFactored -PCFG -compactGrammar 0 -saveToSerializedFile chinesePCFG-simple.ser.gz -maxLength 40 -train $ctb7train -test $ctb7test ) >>& ./serializedParsers.log
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# German Factored binary from Negra (version 2)
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# $negra 3 is the dev set

‎scripts/pos-tagger/Makefile

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