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The 1.5.0 SourceForge models must be fully compatible with the 1.6.0
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.

099s 207s 15607 363s 21970 521s 353s320
load: 7,448s

Component	Model	Perf 1.5.2	Perf 1.5.3	Perf 1.6.0	Tester	Passed	Comment
Sentence Detector	en-sent.bin	load: 0,093s avg: 34945,0 73621,9 sent/s	load: 0,091s099s 73424 avg: 75960, 6 9 sent/s	load: 0,	106s 73586avg: 76133,0 7 sent/s load: 0,097s	William	Yes		False	Output does not match.
Tokenizer	en-token.bin	load: 0,234s avg: 3721,2 6107,0 sent/s	load: 0,193s201s 5529 avg: 6108, 5 4 sent/s	load: 0,	198s 5806avg: 6125,8 9 sent/s load: 0,224s	William	Yes	RC2
Name Finder	en-ner-person.bin 1638,9 sent/s	load: 1,049s133s 1579 avg: 1451, 0 2 sent/s	load: 1, 171s 152s	avg: 1615,	6 sent/s	load: 1,250s	William	Yes	129s avg: 1559,2 sent/s	William	Yes	RC2
POS Tagger	en-pos-maxent.bin	load: 1,764s avg: 1218,9 1402,6 sent/s	load: 1,636s703s 1427 avg: 1469, 0 8 sent/s	load: 1,	435s 1432avg: 1431,3 2 sent/sload: 1,527s	William	Yes	RC2
POS Tagger	en-pos-perceptron.bin 2168,3 sent/s	load: 1,185s309s 2156 avg: 1839, 4 0 sent/s	load: 1, 225s 124s	avg: 2210,	5 sent/s	load: 1,192s170s avg: 2202,7 sent/s	William	Yes	RC2
Chunker	en-chunker.bin	358,2 load: 0,667s avg: 439,5 sent/s	load: 0,563s510s 443 avg: 446,7 sent/s	load: 0,	Failed	William	No	Exception in thread "main" java.lang.IllegalStateException: Must be started first! at opennlp.tools.cmdline.PerformanceMonitor.incrementCounter(PerformanceMonitor.java:68) at opennlp.tools.cmdline.PerformanceMonitor.incrementCounter(PerformanceMonitor.java:77) at opennlp.tools.cmdline.chunker.ChunkerMETool.run(ChunkerMETool.java:77) at opennlp.tools.cmdline.CLI.main(CLI.java:227)	562s avg: 1332,5 sent/s	Jörn	Yes	RC4. Perf was not updated. Should be ok.
Parser	en-parser-chunking.bin	load: 8,353s avg: 27,4 sent/s	load: 7,335s avg: 33	Parser	en-parser-chunking.bin	25,2 sent/s	load: 7,	200s	avg: 44,	3 sent/s	43,5 sent/s load: 7,618s	William	Yes

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	Jörn	Yes	RC2
Tokenizer	en-token.bin	Jörn
POS Tagger	en-pos-maxent.bin	Jörn
POS Tagger	en-pos-perceptron.bin	Jörn
Parser	en-parser-chunking.bin	Jörn

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.

Jörn

Yes

RC4. Perf was not updated. Should be ok.

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	Jörn	Yes	RC2
Tokenizer	en-token.bin	Jörn	Yes	RC2
POS Tagger	en-pos-maxent.bin	Jörn	Yes	RC2
POS Tagger	en-pos-perceptron.bin	Jörn	Yes	RC2
Parser	en-parser-chunking.bin	Jörn	Yes	RC4, there is a small difference due to OPENNLP-669

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.

8768046198267565 6793437733035048 8435980551053485 6267308850090307 71917098445595869206349206349206 7302158273381295 81444332998996998592436974789915 5826210826210826 8640608785887236 8407943453382699 85226885022176728064866823699945 7880665722379604 79717023372436648732106339468303 3573221757322176 50712589073634255351351351351358014705882352942 4230271668822768 55376799322607958033826638477801 3671497584541063 5039787798408487749351323300467 3931391233324258 .5157142857142857OPENNLP-541, OPENNLP-423

Component	Data	Tester	Tagging Perf 1.5.2	Tagging Perf 1.5.3	Tagging Perf 1.6.0	Comment
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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	Old results couldn't be reproduced. Training 1.53 and 1.6.0-rc2 with the same data file. Test will be automated for RC3.
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
Component	Data	Tester	Tagging Perf 1.5.2	Tagging Perf 1.5.3	Tagging Perf 1.6.0	Comment
Name Finder	CONLL 2002 Dutch Person ned.testa	jkosin	Precision: 0.7552941176470588 Recall: 0.4566145092460882 F-Measure: 0.5691489361702128	Precision: 0.7552941176470588 Recall: 0.4566145092460882 F-Measure: 0.5691489361702128
Name Finder	CONLL 2002 Dutch Person ned.testb	jkosin	Precision: 0.8505025125628141 Recall: 0.6165755919854281 F-Measure: 0.7148891235480465	Precision: 0.8505025125628141 Recall: 0.6165755919854281 F-Measure: 0.7148891235480465
Name Finder	CONLL 2002 Dutch Organization ned.testa	jkosin	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	jkosin	Precision: 0.7830374753451677 Recall: 0.4501133786848073 F-Measure: 0.5716342692584593	Precision: 0.7830374753451677 Recall: 0.4501133786848073 F-Measure: 0.5716342692584593
Name Finder	CONLL 2002 Dutch Location ned.testa	jkosin	Precision: 0.8458333333333333 Recall: 0.42379958246346555 F-Measure: 0.564673157162726	Precision: 0.8458333333333333 Recall: 0.42379958246346555 F-Measure: 0.564673157162726
Name Finder	CONLL 2002 Dutch Location ned.testb	jkosin	Precision: 0.8816326530612245 Recall: 0.5581395348837209 F-Measure: 0.6835443037974683	Precision: 0.8816326530612245 Recall: 0.5581395348837209 F-Measure: 0.6835443037974683
Name Finder	CONLL 2002 Dutch Misc ned.testa	jkosin	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	jkosin	Precision: 0.8264984227129337 Recall: 0.44144903117101936 F-Measure: 0.5755079626578803	Precision: 0.8264984227129337 Recall: 0.44144903117101936 F-Measure: 0.5755079626578803
Name Finder	CONLL 2002 Combined ned.testa	jkosin	Precision: 0.6509695290858726 Recall: 0.628822629969419 F-Measure: 0.6397044526540929	Precision: 0.664424218440839 Recall: 0.6418195718654435 F-Measure: 0.6529263076025666		1000 iterations OPENNLP-417
Name Finder	CONLL 2002 Dutch Combined ned.testb	jkosin	Precision: 0.6869929337869668 Recall: 0.6660746003552398 F-Measure: 0.6763720690543674	Precision: 0.7006019366657943 Recall: 0.679269221009896 F-Measure: 0.6897706776603968		1000 iterations OPENNLP-417
Name Finder	CONLL 2002 Spanish Person esp.testa	jkosin	Precision: 0.9010695187165776 Recall: 0.5515548281505729 F-Measure: 0.684263959390863	Precision: 0.9010695187165776 Recall: 0.5515548281505729 F-Measure: 0.684263959390863
Name Finder	CONLL 2002 Spanish Person esp.testb	jkosin	Precision: 0.9195205479452054 Recall: 0.7306122448979592 F-Measure: 0.8142532221379833	Precision: 0.9195205479452054 Recall: 0.7306122448979592 F-Measure: 0.8142532221379833
Name Finder	CONLL 2002 Spanish Organization esp.testa	jkosin	Precision: 0.8288942695722357 Recall: 0.6041176470588235 F-Measure: 0.6988771691051379	Precision: 0.8288942695722357 Recall: 0.6041176470588235 F-Measure: 0.6988771691051379
Name Finder	CONLL 2002 Spanish Organization esp.testb	jkosin	Precision: 0.8036277602523659 Recall: 0.7278571428571429 F-Measure: 0.7638680659670164	Precision: 0.8036277602523659 Recall: 0.7278571428571429 F-Measure: 0.7638680659670164
Name Finder	CONLL 2002 Spanish Location esp.testa	jkosin	Precision: 0.7743016759776536 Recall: 0.7042682926829268 F-Measure: 0.7376263970196913	Precision: 0.7743016759776536 Recall: 0.7042682926829268 F-Measure: 0.7376263970196913
Name Finder	CONLL 2002 Spanish Location esp.testb	jkosin	Precision: 0.8301886792452831 Recall: 0.5682656826568265 F-Measure: 0.6746987951807228	Precision: 0.8301886792452831 Recall: 0.5682656826568265 F-Measure: 0.6746987951807228
Name Finder	CONLL 2002 Spanish Misc esp.testa	jkosin	Precision: 0.6492890995260664 Recall: 0.30786516853932583 F-Measure: 0.4176829268292683	Precision: 0.6492890995260664 Recall: 0.30786516853932583 F-Measure: 0.4176829268292683
Name Finder	CONLL 2002 Spanish Misc esp.testb	jkosin	Precision: 0.686046511627907 Recall: 0.3480825958702065 F-Measure: 0.461839530332681	Precision: 0.686046511627907 Recall: 0.3480825958702065 F-Measure: 0.461839530332681
Name Finder	CONLL 2002 Spanish Combined esp.testa	jkosin	Precision: 0.7005423249233671 Recall: 0.6828315329809239 F-Measure: 0.6915735567970205	Precision: 0.7047866069323273 Recall: 0.6869685129855205 F-Measure: 0.6957635009310986		1000 iterations OPENNLP-417
Name Finder	CONLL 2002 Spanish Combined esp.testb	jkosin	Precision: 0.756635931824532 Recall: 0.7611017425519955 F-Measure: 0.7588622670589884	Precision: 0.7588711930706902 Recall: 0.7633501967397415 F-Measure: 0.7611041053664006		1000 iterations OPENNLP-417
Name Finder	CONLL 2003 English Person Combined eng.testatestb	jkosin	Precision: 0.9523195876288659 8041311831853597 Recall: 0.8023887079261672 7857648725212465 F-Measure: 0.87094873305833827948419450165667	Precision: 0.9523195876288659 8064866823699945 Recall: 0.8023887079261672 7880665722379604 F-Measure: 0.87094873305833827971702337243664		1000 iterations OPENNLP-417
Name Finder	CONLL 2003 English German Person engdeu.testbtesta	jkosinjoern	Precision: 0.9391727493917275 9132653061224489 Recall: 0.7161410018552876 25553176302640973 F-Measure: 0.81263157894736853993307306190742	Precision: 0.9391727493917275 9132653061224489 Recall: 0.7161410018552876 25553176302640973 F-Measure: 0.8126315789473685			Name Finder	CONLL 2003 English Organization eng.testa	jkosin	3993307306190742	Precision: 0.	9132653061224489 Recall: 0.	25553176302640973 F-Measure: 0.3993307306190742
Name Finder	CONLL 2003 German Person deu.testb	joern.7655462184873949	Precision: 0.8768046198267565 8732106339468303 Recall: 0.6793437733035048 3573221757322176 F-Measure: 0.7655462184873949			Name Finder	CONLL 2003 English Organization eng.testb	jkosin	0.507125890736342	Precision: 0.8435980551053485 8732106339468303 Recall: 0.6267308850090307 3573221757322176 F-Measure: 0.7191709844559586507125890736342	Precision: 0.	8732106339468303 Recall: 0.	3573221757322176 F-Measure: 0.	507125890736342
Name Finder	CONLL 2003 English Location engGerman Organization deu.testa	jkosinjoern	Precision: 0.9361421988150099 8407224958949097 Recall: 0.7740881872618399 4125705076551168 F-Measure: 0.84743742550655545535135135135135	Precision: 0.9361421988150099 Recall: 0.7740881872618399 F-Measure: 0.8474374255065554			Name Finder	CONLL 2003 English Location eng.testb	jkosin	8407224958949097 Precision: 0.9206349206349206 Recall: 0.7302158273381295 4125705076551168 F-Measure: 0.81444332998996995535135135135135	Precision: 0.	8407224958949097 Recall: 0.	4125705076551168 F-Measure: 0.	5535135135135135
	Name Finder	CONLL 2003 English Misc eng.testaGerman Organization deu.testb	joernjkosin	Precision: 0.9027982326951399 8014705882352942 Recall: 0.6648590021691974 4230271668822768 F-Measure: 0.76577139287945035537679932260795	Precision: 0.9027982326951399 8014705882352942 Recall: 0.6648590021691974 4230271668822768 F-Measure: 0.7657713928794503			Name Finder	CONLL 2003 English Misc eng.testb	jkosin	5537679932260795	Precision: 0.	8014705882352942 Recall: 0.	4230271668822768 F-Measure: 0.5537679932260795
Name Finder	CONLL 2003 German Location deu.testa	joern.6943972835314092	Precision: 0.8592436974789915 7816326530612245 Recall: 0.5826210826210826 32430143945808637 F-Measure: 0.6943972835314092			Name Finder	CONLL 2003 English Combined eng.testa	jkosin45840813883901854	Precision: 0.861812521618817 7816326530612245 Recall: 0.8386065297879501 32430143945808637 F-Measure: 0.850051177072671445840813883901854	Precision: 0.	7816326530612245 Recall: 0.	32430143945808637 F-Measure: 0.	45840813883901854		1000 iterations OPENNLP-417
Name Finder	CONLL 2003 English Combined eng.testbjkosinGerman Location deu.testb	joern	Precision: 0.8033826638477801 Recall: 0.3671497584541063 F-Measure: 0.5039787798408487	Precision: 0.8041311831853597 8033826638477801 Recall: 0.7857648725212465 3671497584541063 F-Measure: 0.79484194501656675039787798408487	Precision: 0.	8033826638477801 Recall: 0.	3671497584541063 F-Measure: 0.	5039787798408487		1000 iterations OPENNLP-417
Name Finder	CONLL 2003 German Person Misc deu.testajkosin	joern	Precision: 0.9132653061224489 7055555555555556 Recall: 0.25553176302640973 12574257425742574 F-Measure: 0.399330730619074221344537815126052	Precision: 0.9132653061224489 7055555555555556 Recall: 0.25553176302640973 12574257425742574 F-Measure: 0.3993307306190742			Name Finder	CONLL 2003 German Person deu.testb	jkosin	Precision: 0.8732106339468303 Recall: 0.3573221757322176 F-Measure: 0.50712589073634221344537815126052	Precision: 0.	7055555555555556 Recall: 0.	12574257425742574 F-Measure: 0.	21344537815126052
Name Finder	CONLL 2003 German Organization Misc deu.testatestb	jkosinjoern	Precision: 0.8407224958949097 6601307189542484 Recall: 0.4125705076551168 15074626865671642 F-Measure: 0.55351351351351352454434993924666	Precision: 0.8407224958949097 6601307189542484 Recall: 0.4125705076551168 15074626865671642 F-Measure: 0.2454434993924666	Precision: 0.6601307189542484 Recall: 0.	15074626865671642 F-Measure: 0.2454434993924666
Name Finder	CONLL 2003 German Organization deu.testbjkosinCombined deu.testa	joern	Precision: 0.7718859429714857 Recall: 0.319263397475688 F-Measure: 0.4516978922716628	Precision: 0.8014705882352942 7783891945972986 Recall: 0.4230271668822768 32195323815435545 F-Measure: 0.553767993226079545550351288056207	Precision: 0.	7783891945972986 Recall: 0.	32195323815435545 F-Measure: 0.	45550351288056207	OPENNLP-417
Name Finder	CONLL 2003 German Location Combined deu.testatestb	jkosinjoern	Precision: 0.7816326530612245 7467566165023353 Recall: 0.32430143945808637 3917778382793357 F-Measure: 0.458408138839018545139285714285715	Precision: 0.7816326530612245 749351323300467 Recall: 0.32430143945808637 3931391233324258 F-Measure: 0.45840813883901854			Name Finder	CONLL 2003 German Location deu.testb	jkosin5157142857142857	Precision: 0.	749351323300467 Recall: 0.	3931391233324258 F-Measure: 0.5157142857142857	OPENNLP-417
POS Tagger	CONLL 2006 Danish	joern	Accuracy5039787798408487Precision: 0.8033826638477801 9511278195488722 Recall	Accuracy: 0.3671497584541063 9512987012987013 F-Measure	Accuracy: 0.
Name Finder	CONLL 2003 German Misc deu.testa	jkosin	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	jkosin	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	jkosin	Precision: 0.7718859429714857 Recall: 0.319263397475688 F-Measure: 0.4516978922716628	Precision: 0.7783891945972986 Recall: 0.32195323815435545 F-Measure: 0.45550351288056207		OPENNLP-417
9512987012987013	Test will be automated for RC3.
POS Tagger	CONLL 2006 Dutch	joern	Accuracy: 0.9324977618621307	Accuracy: 0.9324977618621307	Accuracy: 0.9174574753804834	Retrained 1.5.3 and got same result as for 1.6.0-rc2. Test will be automated for RC3.
POS Tagger	CONLL 2006 Portuguese	joern	Accuracy: 0.9659110277825124	Accuracy: 0.9659110277825124	Accuracy: 0.9659110277825124	Test will be automated for RC3.
POS Tagger	CONLL 2006 Swedish	joern	Accuracy: 0.9275106082036775	Accuracy: 0.9275106082036775	Accuracy: 0.9275106082036775	Test will be automated for RC3.
Chunker	CONLL 2000	William	Precision: 0.9257575757575758 Recall: 0.9221868187154117 F-Measure: 0.9239687473746113	Precision: 0.9257575757575758 Recall: 0.9221868187154117	Name Finder	CONLL 2003 German Combined deu.testb	jkosin	Precision: 0.7467566165023353 Recall: 0.3917778382793357 F-Measure: 0.51392857142857159239687473746113	Precision: 0.	9257575757575758 Recall: 0.	9221868187154117 F-Measure: 0		OPENNLP-417
POS Tagger	CONLL 2006 Danish	Jörn / ?	Accuracy: 0.9511278195488722	Accuracy: 0.9512987012987013		Jörn: Same result as other tester
POS Tagger	CONLL 2006 Dutch	Jörn	Accuracy: 0.9324977618621307	Accuracy: 0.9324977618621307
POS Tagger	CONLL 2006 Portuguese	Jörn / ?	Accuracy: 0.9659110277825124	Accuracy: 0.9659110277825124		Jörn: Same result as other tester
POS Tagger	CONLL 2006 Swedish	Jörn	Accuracy: 0.9275106082036775	Accuracy: 0.9275106082036775
.9239687473746113	Test will be automated for RC3.
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	PERCEPTRON Cutoff 0 Test will be automated for RC3.
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	PERCEPTRON Cutoff 0 alphaNumOpt Test will be automated for RC3.
Chunker	Arvores Deitadas (10-fold cross-validation)	Chunker	CONLL 2000	William	Precision: 0.9257575757575758 9404684925220583 Recall: 0.9221868187154117 9374181341871635 F-Measure: 0.92396874737461139389408359191154	Precision: 0.9257575757575758 9562405864042575 Recall: 0.9221868187154117 9582419351592844 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		PERCEPTRON Cutoff 0 1.5.2 works poorly because we didn't have configurable EOS
Tokenizer	Arvores Deitadas (Floresta Virgem) (10-fold cross-validation)	William		Precision: 0.9995231988260895 Recall: 0.9994542652270997 F-Measure: 0.9994887308380267		PERCEPTRON Cutoff 0 alphaNumOpt
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
9572402147035765	Precision: 0.9562712068584737 Recall: 0.9585035519510575 F-Measure: 0.9573860781121228	Retrained 1.5.3 and got same result as for 1.6.0-rc2. Test will be automated for RC3.

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
OntoNotes4*	Jörn	Yes
ConllL2002*	Jörn	Yes
ConllX*	Jörn	Yes
Arvores*	William	Yes
Conll00*	William	Yes

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.

...

Package	File or Test	Tester	Passed	Comment
Binary	LICENSE	William	NoNeeds	review. We don't distribute JWNL.JWNL license should be removed. Snowball license is missing. Fixed in trunk!
Binary	NOTICE	WilliamJörn	No	Yes	Copyright 2010, 2013. We don't distribute JWNL.
Binary	README	WilliamJörn	No	Needs reviewCoref mention should be removed. Fixed in trunk!
Binary	RELEASE_NOTES.html	William	Yes
Binary	Test signatures: .md5, .sha1, .asc	Jörn
Binary	JIRA issue list created	William	Yes
Binary	Contains maxent, the tools , uima and jwnl jars	William	No	jar and documentation?	Jörn	Yes	RC4It contains only tools and uima, is it correct?
Source	LICENSE	Jörn	Yes
Source	NOTICE	Jörn	Yes
Source	Test signatures: .md5, .sha1, .asc	Jörn
Source	Can build from source?	Jörn	Yes	RC4

Notes about testing

Compatibility tests

...

Child pages

Versions Compared

Old Version 9

New Version Current

Key

Regression Test Training with (private) English data

Performance test with public data

Regression Test Training with (private) English data

Performance test with public data

Automated Evaluation Testing

Test UIMA Integration

Notes about testing

Compatibility tests