Test Plan for Apache OpenNLP 1.7.0

This page contains the test plan for the 1.7.0 release.

The 1.7.0 release introduces API changes.

Apache OpenNLP 1.7.0 requires Java 1.8.

Compatibility Test with OpenNLP 1.5.0 SourceForge Models

The 1.5.0 SourceForge models must be fully compatible with the 1.7.0
release. In this test all the English models are tested for compatibility
on the English 300k sentences Leipzig Corpus. It is tested that
the output produced with the same model by both versions has the same md5 hash.

Load: the time in seconds to Apache OpenNLP took to load the model.
Avg: the average throughput of the module, in sentences per second.

Component	Model	Perf 1.5.2	Perf 1.5.3	Perf 1.6.0
Sentence Detector	en-sent.bin	load: 0,093s avg: 34945,0 sent/s	load: 0,099s avg: 75960,9 sent/s	load: 0,106s avg: 76133,7 sent/s
Tokenizer	en-token.bin	load: 0,234s avg: 3721,2 sent/s	load: 0,201s avg: 6108,4 sent/s	load: 0,198s avg: 6125,9 sent/s
Name Finder	en-ner-person.bin	load: 1,133s avg: 1451,2 sent/s	load: 1,152s avg: 1615,6 sent/s	load: 1,129s avg: 1559,2 sent/s
POS Tagger	en-pos-maxent.bin	load: 1,764s avg: 1218,9 sent/s	load: 1,703s avg: 1469,8 sent/s	load: 1,435s avg: 1431,2 sent/s
POS Tagger	en-pos-perceptron.bin	load: 1,309s avg: 1839,0 sent/s	load: 1,124s avg: 2210,5 sent/s	load: 1,170s avg: 2202,7 sent/s
Chunker	en-chunker.bin	load: 0,667s avg: 439,5 sent/s	load: 0,510s avg: 446,7 sent/s	load: 0,562s avg: 1332,5 sent/s
Parser	en-parser-chunking.bin	load: 8,353s avg: 27,4 sent/s	load: 7,335s avg: 33,2 sent/s	load: 7,200s avg: 44,3 sent/s

Note: Test was done on MacBook Pro 15", 2 GHz Core i7, 16GB Ram, 500GB HD running OS X 10.10.1
and Java 1.7.0_71-b14. The performance varies because light weight tasks have been performed in the background while testing.

Note: "Concurrent" in the comment means that both tests where started at the same time.

Regression Test Training with (private) English data

The training of both versions with the same data must produce
a model with identical output. The model output is tested with
the procedure from the previous test.

To pass the test the event hash and the model output must be identical.

Component	Model	Tester	Passed	Comment
Sentence Detector	en-sent.bin
Tokenizer	en-token.bin
POS Tagger	en-pos-maxent.bin
POS Tagger	en-pos-perceptron.bin
Parser	en-parser-chunking.bin

Note: Time was measured with the time command, the value is the "real" time value.

Performance test with public data

Test the tagging performance with all the publicly available training
and test data for various languages.

It is assumed that the training will be done with a cutoff of 5 and 100 iterations,
if different values are used please write them into the comment.

Component	Data	Tester	Tagging Perf 1.5.2	Tagging Perf 1.5.3	Tagging Perf 1.6.0
Name Finder	CONLL 2002 Dutch Person ned.testa	joern		Precision: 0.7570754716981132 Recall: 0.4566145092460882 F-Measure: 0.5696539485359361	Precision: 0.7570754716981132 Recall: 0.4566145092460882 F-Measure: 0.5696539485359361
Name Finder	CONLL 2002 Dutch Person ned.testb	joern		Precision: 0.8479899497487438 Recall: 0.6147540983606558 F-Measure: 0.7127771911298839	Precision: 0.8479899497487438 Recall: 0.6147540983606558 F-Measure: 0.7127771911298839
Name Finder	CONLL 2002 Dutch Organization ned.testa	joern		Precision: 0.8561872909698997 Recall: 0.37317784256559766 F-Measure: 0.5197969543147207	Precision: 0.8561872909698997 Recall: 0.37317784256559766 F-Measure: 0.5197969543147207
Name Finder	CONLL 2002 Dutch Organization ned.testb	joern		Precision: 0.783203125 Recall: 0.4546485260770975 F-Measure: 0.5753228120516498	Precision: 0.783203125 Recall: 0.4546485260770975 F-Measure: 0.5753228120516498
Name Finder	CONLL 2002 Dutch Location ned.testa	joern		Precision: 0.8427947598253275 Recall: 0.40292275574112735 F-Measure: 0.5451977401129944	Precision: 0.8427947598253275 Recall: 0.40292275574112735 F-Measure: 0.5451977401129944
Name Finder	CONLL 2002 Dutch Location ned.testb	joern		Precision: 0.8827160493827161 Recall: 0.5542635658914729 F-Measure: 0.680952380952381	Precision: 0.8827160493827161 Recall: 0.5542635658914729 F-Measure: 0.680952380952381
Name Finder	CONLL 2002 Dutch Misc ned.testa	joern		Precision: 0.8354114713216958 Recall: 0.44786096256684493 F-Measure: 0.5831157528285466	Precision: 0.8354114713216958 Recall: 0.44786096256684493 F-Measure: 0.5831157528285466
Name Finder	CONLL 2002 Dutch Misc ned.testb	joern		Precision: 0.8354114713216958 Recall: 0.44786096256684493 F-Measure: 0.5831157528285466	Precision: 0.8354114713216958 Recall: 0.44786096256684493 F-Measure: 0.5831157528285466
Name Finder	CONLL 2002 Combined ned.testa	joern		Precision: 0.691407825736184 Recall: 0.6551987767584098 F-Measure: 0.6728164867517175	Precision: 0.691407825736184 Recall: 0.6551987767584098 F-Measure: 0.6728164867517175
Name Finder	CONLL 2002 Dutch Combined ned.testb	joern		Precision: 0.7128895932382462 Recall: 0.6848515605176351 F-Measure: 0.6985893619774816	Precision: 0.7128895932382462 Recall: 0.6848515605176351 F-Measure: 0.6985893619774816
Name Finder	CONLL 2002 Spanish Person esp.testa	joern		Precision: 0.9038718291054739 Recall: 0.5540098199672667 F-Measure: 0.686960933536276	Precision: 0.9038718291054739 Recall: 0.5540098199672667 F-Measure: 0.686960933536276
Name Finder	CONLL 2002 Spanish Person esp.testb	joern		Precision: 0.9063545150501672 Recall: 0.7374149659863946 F-Measure: 0.8132033008252063	Precision: 0.9063545150501672 Recall: 0.7374149659863946 F-Measure: 0.8132033008252063
Name Finder	CONLL 2002 Spanish Organization esp.testa	jkosin		Precision: 0.8292880258899676 Recall: 0.6029411764705882 F-Measure: 0.6982288828337874	Precision: 0.8292880258899676 Recall: 0.6029411764705882 F-Measure: 0.6982288828337874
Name Finder	CONLL 2002 Spanish Organization esp.testb	joern		Precision: 0.8031496062992126 Recall: 0.7285714285714285 F-Measure: 0.7640449438202247	Precision: 0.8031496062992126 Recall: 0.7285714285714285 F-Measure: 0.7640449438202247
Name Finder	CONLL 2002 Spanish Location esp.testa	joern		Precision: 0.7754189944134078 Recall: 0.7052845528455285 F-Measure: 0.7386907929749867	Precision: 0.7754189944134078 Recall: 0.7052845528455285 F-Measure: 0.7386907929749867
Name Finder	CONLL 2002 Spanish Location esp.testb	jkosin		Precision: 0.8360433604336044 Recall: 0.5691881918819188 F-Measure: 0.6772777167947311	Precision: 0.8360433604336044 Recall: 0.5691881918819188 F-Measure: 0.6772777167947311
Name Finder	CONLL 2002 Spanish Misc esp.testa	jkosin		Precision: 0.6308411214953271 Recall: 0.30337078651685395 F-Measure: 0.40971168437025796	Precision: 0.6308411214953271 Recall: 0.30337078651685395 F-Measure: 0.40971168437025796
Name Finder	CONLL 2002 Spanish Misc esp.testb	jkosin		Precision: 0.6763005780346821 Recall: 0.34513274336283184 F-Measure: 0.45703124999999994	Precision: 0.6763005780346821 Recall: 0.34513274336283184 F-Measure: 0.45703124999999994
Name Finder	CONLL 2002 Spanish Combined esp.testa	joern		Precision: 0.7213977979894687 Recall: 0.6927143185474604 F-Measure: 0.706765154179857	Precision: 0.7213977979894687 Recall: 0.6927143185474604 F-Measure: 0.706765154179857
Name Finder	CONLL 2002 Spanish Combined esp.testb	joern		Precision: 0.7612574341546304 Recall: 0.7554806070826307 F-Measure: 0.7583580194667795	Precision: 0.7612574341546304 Recall: 0.7554806070826307 F-Measure: 0.7583580194667795
Name Finder	CONLL 2003 English Person eng.testa	jkosin	Precision: 0.9523195876288659 Recall: 0.8023887079261672 F-Measure: 0.8709487330583382	Precision: 0.9523195876288659 Recall: 0.8023887079261672 F-Measure: 0.8709487330583382
Name Finder	CONLL 2003 English Person eng.testb	jkosin	Precision: 0.9391727493917275 Recall: 0.7161410018552876 F-Measure: 0.8126315789473685	Precision: 0.9391727493917275 Recall: 0.7161410018552876 F-Measure: 0.8126315789473685
Name Finder	CONLL 2003 English Organization eng.testa	jkosin	Precision: 0.8768046198267565 Recall: 0.6793437733035048 F-Measure: 0.7655462184873949	Precision: 0.8768046198267565 Recall: 0.6793437733035048 F-Measure: 0.7655462184873949
Name Finder	CONLL 2003 English Organization eng.testb	jkosin	Precision: 0.8435980551053485 Recall: 0.6267308850090307 F-Measure: 0.7191709844559586	Precision: 0.8435980551053485 Recall: 0.6267308850090307 F-Measure: 0.7191709844559586
Name Finder	CONLL 2003 English Location eng.testa	jkosin	Precision: 0.9361421988150099 Recall: 0.7740881872618399 F-Measure: 0.8474374255065554	Precision: 0.9361421988150099 Recall: 0.7740881872618399 F-Measure: 0.8474374255065554
Name Finder	CONLL 2003 English Location eng.testb	jkosin	Precision: 0.9206349206349206 Recall: 0.7302158273381295 F-Measure: 0.8144433299899699	Precision: 0.9206349206349206 Recall: 0.7302158273381295 F-Measure: 0.8144433299899699
Name Finder	CONLL 2003 English Misc eng.testa	jkosin	Precision: 0.9027982326951399 Recall: 0.6648590021691974 F-Measure: 0.7657713928794503	Precision: 0.9027982326951399 Recall: 0.6648590021691974 F-Measure: 0.7657713928794503
Name Finder	CONLL 2003 English Misc eng.testb	jkosin	Precision: 0.8592436974789915 Recall: 0.5826210826210826 F-Measure: 0.6943972835314092	Precision: 0.8592436974789915 Recall: 0.5826210826210826 F-Measure: 0.6943972835314092
Name Finder	CONLL 2003 English Combined eng.testa	jkosin	Precision: 0.861812521618817 Recall: 0.8386065297879501 F-Measure: 0.8500511770726714	Precision: 0.8640608785887236 Recall: 0.8407943453382699 F-Measure: 0.8522688502217672
Name Finder	CONLL 2003 English Combined eng.testb	jkosin	Precision: 0.8041311831853597 Recall: 0.7857648725212465 F-Measure: 0.7948419450165667	Precision: 0.8064866823699945 Recall: 0.7880665722379604 F-Measure: 0.7971702337243664
Name Finder	CONLL 2003 German Person deu.testa	joern	Precision: 0.9132653061224489 Recall: 0.25553176302640973 F-Measure: 0.3993307306190742	Precision: 0.9132653061224489 Recall: 0.25553176302640973 F-Measure: 0.3993307306190742	Precision: 0.9132653061224489 Recall: 0.25553176302640973 F-Measure: 0.3993307306190742
Name Finder	CONLL 2003 German Person deu.testb	joern	Precision: 0.8732106339468303 Recall: 0.3573221757322176 F-Measure: 0.507125890736342	Precision: 0.8732106339468303 Recall: 0.3573221757322176 F-Measure: 0.507125890736342	Precision: 0.8732106339468303 Recall: 0.3573221757322176 F-Measure: 0.507125890736342
Name Finder	CONLL 2003 German Organization deu.testa	joern	Precision: 0.8407224958949097 Recall: 0.4125705076551168 F-Measure: 0.5535135135135135	Precision: 0.8407224958949097 Recall: 0.4125705076551168 F-Measure: 0.5535135135135135	Precision: 0.8407224958949097 Recall: 0.4125705076551168 F-Measure: 0.5535135135135135
Name Finder	CONLL 2003 German Organization deu.testb	joern	Precision: 0.8014705882352942 Recall: 0.4230271668822768 F-Measure: 0.5537679932260795	Precision: 0.8014705882352942 Recall: 0.4230271668822768 F-Measure: 0.5537679932260795	Precision: 0.8014705882352942 Recall: 0.4230271668822768 F-Measure: 0.5537679932260795
Name Finder	CONLL 2003 German Location deu.testa	joern	Precision: 0.7816326530612245 Recall: 0.32430143945808637 F-Measure: 0.45840813883901854	Precision: 0.7816326530612245 Recall: 0.32430143945808637 F-Measure: 0.45840813883901854	Precision: 0.7816326530612245 Recall: 0.32430143945808637 F-Measure: 0.45840813883901854
Name Finder	CONLL 2003 German Location deu.testb	joern	Precision: 0.8033826638477801 Recall: 0.3671497584541063 F-Measure: 0.5039787798408487	Precision: 0.8033826638477801 Recall: 0.3671497584541063 F-Measure: 0.5039787798408487	Precision: 0.8033826638477801 Recall: 0.3671497584541063 F-Measure: 0.5039787798408487
Name Finder	CONLL 2003 German Misc deu.testa	joern	Precision: 0.7055555555555556 Recall: 0.12574257425742574 F-Measure: 0.21344537815126052	Precision: 0.7055555555555556 Recall: 0.12574257425742574 F-Measure: 0.21344537815126052	Precision: 0.7055555555555556 Recall: 0.12574257425742574 F-Measure: 0.21344537815126052
Name Finder	CONLL 2003 German Misc deu.testb	joern	Precision: 0.6601307189542484 Recall: 0.15074626865671642 F-Measure: 0.2454434993924666	Precision: 0.6601307189542484 Recall: 0.15074626865671642 F-Measure: 0.2454434993924666	Precision: 0.6601307189542484 Recall: 0.15074626865671642 F-Measure: 0.2454434993924666
Name Finder	CONLL 2003 German Combined deu.testa	joern	Precision: 0.7718859429714857 Recall: 0.319263397475688 F-Measure: 0.4516978922716628	Precision: 0.7783891945972986 Recall: 0.32195323815435545 F-Measure: 0.45550351288056207	Precision: 0.7783891945972986 Recall: 0.32195323815435545 F-Measure: 0.45550351288056207
Name Finder	CONLL 2003 German Combined deu.testb	joern	Precision: 0.7467566165023353 Recall: 0.3917778382793357 F-Measure: 0.5139285714285715	Precision: 0.749351323300467 Recall: 0.3931391233324258 F-Measure: 0.5157142857142857	Precision: 0.749351323300467 Recall: 0.3931391233324258 F-Measure: 0.5157142857142857
POS Tagger	CONLL 2006 Danish	joern	Accuracy: 0.9511278195488722	Accuracy: 0.9512987012987013	Accuracy: 0.9512987012987013
POS Tagger	CONLL 2006 Dutch	joern	Accuracy: 0.9324977618621307	Accuracy: 0.9324977618621307	Accuracy: 0.9174574753804834
POS Tagger	CONLL 2006 Portuguese	joern	Accuracy: 0.9659110277825124	Accuracy: 0.9659110277825124	Accuracy: 0.9659110277825124
POS Tagger	CONLL 2006 Swedish	joern	Accuracy: 0.9275106082036775	Accuracy: 0.9275106082036775	Accuracy: 0.9275106082036775
Chunker	CONLL 2000	William	Precision: 0.9257575757575758 Recall: 0.9221868187154117 F-Measure: 0.9239687473746113	Precision: 0.9257575757575758 Recall: 0.9221868187154117 F-Measure: 0.9239687473746113	Precision: 0.9257575757575758 Recall: 0.9221868187154117 F-Measure: 0.9239687473746113
Sentence Detector	Arvores Deitadas (Floresta Virgem) (10-fold cross-validation)	William		Precision: 0.9891491491491492 Recall: 0.9894066523820013 F-Measure: 0.9892778840089301	Precision: 0.9891491491491492 Recall: 0.9894066523820013 F-Measure: 0.9892778840089301
Tokenizer	Arvores Deitadas (Floresta Virgem) (10-fold cross-validation)	William		Precision: 0.9995231988260895 Recall: 0.9994542652270997 F-Measure: 0.9994887308380267	Precision: 0.9995231988260895 Recall: 0.9994542652270997 F-Measure: 0.9994887308380267
Chunker	Arvores Deitadas (10-fold cross-validation)	William	Precision: 0.9404684925220583 Recall: 0.9374181341871635 F-Measure: 0.9389408359191154	Precision: 0.9562405864042575 Recall: 0.9582419351592844 F-Measure: 0.9572402147035765	Precision: 0.9562712068584737 Recall: 0.9585035519510575 F-Measure: 0.9573860781121228

Automated Evaluation Testing

The Conll 2002 tests for example can be run with this command:

mvn test -DOPENNLP_DATA_DIR=~/opennlp-test-data/ -Dtest=Conll02*

Unit Tests	Tester	Passed	Comment
OntoNotes4*	Jörn
ConllL2002*	Jörn
ConllX*	Jörn
Arvores*	William
Conll00*	William

Test UIMA Integration

The test ensures that the Analysis Engine can run and not not
crash trough simple runtime time code errors. We need to add
more sophisticated testing with the next releases.

Analysis Engine	Tester	Passed	Comment
Sentence Detector
Sentence Detector Trainer
Tokenizer ME
Tokenizer Trainer
Name Finder
Name Finder Trainer
Chunker
Chunker Trainer
POS Tagger
POS Tagger Trainer
Parser
createPear.sh
Sample PEAR

Distribution Review

Please ensure that the listed files below are included in the distributions
and are in a good state.

Package	File or Test	Tester	Passed	Comment
Binary	LICENSE
Binary	NOTICE
Binary	README
Binary	RELEASE_NOTES.html
Binary	Test signatures: .md5, .sha1, .asc
Binary	JIRA issue list created
Binary	Contains the tools jar and documentation?
Source	LICENSE
Source	NOTICE
Source	Test signatures: .md5, .sha1, .asc
Source	Can build from source?

Notes about testing

Compatibility tests

The following commands can be used to reproduce the compatibility tests with Leipzig corpus.

# Corpus preparation: the following command will create documents from the corpus. Sed is used to remove the language prefix

sh bin/opennlp DoccatConverter leipzig -data ../eng_news_2010_300K-text/eng_news_2010_300K-sentences.txt -encoding UTF-8 -lang en | sed -E 's/^en[[:space:]]//g' > ../out-tokenized-documents.test

# Corpus preparation: this forces the detokenization of the documents

sh bin/opennlp SentenceDetectorConverter namefinder -data ../out-tokenized-documents.test -encoding UTF-8 -detokenizer trunk/opennlp-tools/lang/en/tokenizer/en-detokenizer.xml > ../out-documents.test

# Now the actually tests. Execute it for the previous release and for the current RC. Compare the output using diff:

time sh bin/opennlp SentenceDetector ../models/en-sent.bin < ../out-documents.test > ../out-sentences_1.5.2.test

time sh bin/opennlp TokenizerME ../models/en-token.bin < ../out-sentences_1.5.2.test > ../out-toks_1.5.2.test

time sh bin/opennlp TokenNameFinder ../models/en-ner-person.bin < ../out-toks_1.5.2.test > ../out-ner_1.5.2.test

time sh bin/opennlp POSTagger ../models/en-pos-maxent.bin < ../out-toks_1.5.2.test > ../out-pos_maxent_1.5.2.test

time sh bin/opennlp POSTagger ../models/en-pos-perceptron.bin < ../out-toks_1.5.2.test > ../out-pos_pers_1.5.2.test

time sh bin/opennlp ChunkerME ../models/en-chunker.bin < ../out-pos_pers_1.5.2.test > ../out-chk_1.5.2.test

time sh bin/opennlp Parser ../models/en-parser-chunking.bin < ../out-toks_1.5.2.test > ../out-parse_1.5.2.test

Child pages

TestPlan1.7.0