Differences between revisions 3 and 71 (spanning 68 versions)

Benchmarks

This page documents performance of most popular contemporary NLP systems for Polish.

Single-word lemmatization and morphological analysis

System name and URL	Approach	Main publication	License	P	R	F
Morfeusz		Woliński, M. Morfeusz — a practical tool for the morphological analysis of Polish. In M.A. Kłopotek, S.T. Wierzchoń, K. Trojanowski (eds.) Proceedings of the International IIS:IIPWM 2006 Conference, pp. 511–520, Wisła, Poland, 2006.	2-clause BSD	%	%	%
Morfologik		Miłkowski M. Developing an open-source, rule-based proofreading tool. Software: Practice and Experience, 40(7):543–566, 2010.		%	%	%
LemmaPL	dictionary-based rules and heuristics	Kobyliński Ł. (unpublished)	GPL	%	%	%

Multi-word lemmatization

System name and URL	Approach	Main publication	License	Accuracy
–	rule-based	Degórski, Ł. Towards the lemmatisation of Polish nominal syntactic groups using a shallow grammar. In P. Bouvry, M.A. Kłopotek, F. Leprevost, M. Marciniak, A. Mykowiecka, H. Rybiński (eds.) Security and Intelligent Information Systems, Lecture Notes in Computer Science vol. 7053, pp. 370–378, Springer-Verlag Berlin Heidelberg, 2012.	?	82.90%
–	automatic generation of lemmatization rules using CRF	Radziszewski A. Learning to lemmatise Polish noun phrases. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Volume 1: Long Papers. ACL, pp. 701–709.	GPL	80.70%
–	automatic generation of lemmatization rules from a corpus	Abramowicz W., Filipowska A., Małyszko J., Wagner T. Lemmatization of Multi-Word Entity Names for Polish Language Using Rules Automatically Generated Based on the Corpus Analysis. In Z. Vetulani, J. Mariani (eds.) Human Language Technologies as a Challenge for Computer Science and Linguistics, Fundacja Uniwersytetu im. A. Mickiewicza, pp. 540–544, Poznań 2015.	?	82.10%
PoLem	dictionary-based rules and heuristics	Marcińczuk M. (in print)	GPL	97.99%

Disambiguated POS tagging

The comparisons are performed using plain text as input and reporting the accuracy lower bound (Acc_lower) metric proposed by Radziszewski and Acedański (2012). The metric penalizes all segmentation changes in regard to the gold standard and treats such tokens as misclassified. Furthermore, we report separate metric values for both known and unknown words to assess the performance of guesser modules built into the taggers. These are indicated as Acc^K_lower for known and Acc^U_lower for unknown words.

The experiments have been performed on the manually annotated part of the National Corpus of Polish v. 1.1 (1M tokens). The ten-fold cross-validation procedure has been followed, by re-evaluating the methods ten times, each time selecting one of ten parts of the corpus for testing and the remaining parts for training the taggers. The provided results are averages calculated over ten training and testing sequences. Each of the taggers and each tagger ensemble has been trained and tested on the same set of cross-validation folds, so the results are directly comparable. Each of the training folds has been reanalyzed, according to the procedure described in (A Tiered CRF Tagger for Polish, using the Maca toolkit (Radziszewski and Śniatowski 2011). The idea of a morphological reanalysis of the gold-standard data is to allow the trained tagger to see similar input that is expected in the tagging phase. The training data is firstly turned into plain text and analyzed using the same mechanism that will be used by the tagger during actual tagging process. The output of the analyzer is then synchronized with the original gold-standard data, by using the original tokenization. Tokens with changed segmentation are taken from the gold-standard intact. In the case of tokens for which the segmentation did not change in the process of morphological analysis, the produced interpretations are compared with the original. A token is marked as an unknown word if the correct interpretation has not been produced by the analyzer. Maca has been run with the morfeusz-nkjp-official configuration, which uses Morfeusz SGJP analyzer (Woliński 2006) and no guesser module.

Tagger efficiency was compared by measuring training and tagging times of each of the methods on the same machine. 1.1M token set was used both for training and tagging stages. The total processing time included model loading/saving time and other I/O operations (e.g. reading/writing the tokens).

System name and URL	Approach	Main publication	License	Acc_lower	Acc^K_lower	Acc^U_lower	Training time	Tagging time
OpenNLP	MaxEnt model	Kobyliński Ł., Kieraś W. Part of Speech Tagging for Polish: State of the Art and Future Perspectives. In Proceedings of the 17th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2016), Konya, Turkey, 2016.	GPL	87.24%	88.02%	62.05%	11095 s	362 s
Pantera	rule-based adapted Brill tagger	Acedański S. A Morphosyntactic Brill Tagger for Inflectional Languages. In H. Loftsson, E. Rögnvaldsson, S. Helgadóttir (eds.) Advances in Natural Language Processing, LNCS 6233, pp. 3–14, Springer, 2010.	GPL 3	88.95%	91.22%	15.19%	2624 s	186 s
WMBT	memory-based	Radziszewski A., Śniatowski T. A memory-based tagger for Polish. In: Z. Vetulani (ed.) Proceedings of the 5th Language and Technology Conference (LTC 2011), pp. 556–560, Poznań, Poland.		90.33%	91.26%	60.25%	548 s	4338 s
WCRFT	tiered, CRF-based	Radziszewski A. A Tiered CRF Tagger for Polish. In R. Bembenik, Ł. Skonieczny, H. Rybiński, M. Kryszkiewicz, M. Niezgódka (eds.) Intelligent Tools for Building a Scientific Information Platform, pp. 215–230, Springer Berlin Heidelberg, 2013.	LGPL 3.0	90.76%	91.92%	53.18%	27242 s	420 s
Concraft	mutually dependent CRF layers	Waszczuk J. Harnessing the CRF complexity with domain-specific constraints. The case of morphosyntactic tagging of a highly inflected language. In Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), pp. 2789–2804, Mumbai, India, 2012.	2-clause BSD	91.07%	92.06%	58.81%	26675 s	403 s
PoliTa	voting ensemble	Kobyliński Ł. PoliTa: A multitagger for Polish. In N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, S. Piperidis (eds.) Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014), pp. 2949–2954, Reykjavík, Iceland, ELRA, 2014.	GPL	92.01%	92.91%	62.81%	N/A	N/A

Dependency parsing

10-fold cross-validation was used to test the quality of MaltParser and MateParser on Polish data (average LAS and UAS scores of 10 tests are provided).

System name and URL	Approach	Main publication	License	LAS	UAS
Polish Dependency parser (MaltParser)	trained on the extended version of the Polish dependency treebank with MaltParser	Wróblewska A. Polish Dependency Parser Trained on an Automatically Induced Dependency Bank. PhD dissertation, Institute of Computer Science, Polish Academy of Sciences, Warsaw, 2014.	GPL 3	84%	89%
Polish Dependency parser (MateParser)	trained on the same data with MateParser		GPL 3	89%	93%
Google parser				%	%

Shallow parsing

System name and URL	Approach	Main publication	License	P	R	F
Spejd	rule-based	Buczyński A., Przepiórkowski A. Spejd: A shallow processing and morphological disambiguation tool. In Z. Vetulani, H. Uszkoreit (eds.) Human Language Technology: Challenges of the Information Society, LNCS 5603, pp. 131–141. Springer-Verlag, Berlin, 2009.	GPL 3	%	%	%

Deep parsing

System name and URL	Approach	Main publication	License	P	R	F
Świgra				%	%	%
POLFIE			GPL 3 (grammar)	%	%	%
ENIAM			GPL 3	%	%	%

Word sense disambiguation

Manually annotated subcorpus of the National Corpus of Polish with 3889 texts and 1217822 segments was used for training and test (cross-validation); it contained 34186 occurrences of multi-sense words (1–7 senses). Simple heuristics selecting the most frequent sense resulted in 78.3% accuracy. The table presents results of leave-one-out evaluation with individual selection of each ambiguous words (as described in the publication).

System name and URL	Approach	Main publication	License	Accuracy
WSDDE	Machine-learning	Kopeć M., Młodzki R., Przepiórkowski A. Automatyczne znakowanie sensami słów. In A. Przepiórkowski, M. Bańko, R.L. Górski, B. Lewandowska-Tomaszczyk (eds.) Narodowy Korpus Języka Polskiego, pp. 209–224. Wydawnictwo Naukowe PWN, Warsaw, 2012.	GPL 3	91.46%
Babelfy	BabelNet-powered	?	?	?

Named entity recognition

System name and URL	Approach	Main publication	License	P	R	F
NERF linear-chain CRF		Savary A., Waszczuk J., Przepiórkowski A. Towards the annotation of named entities in the National Corpus of Polish. In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC 2010), pp. 3622–3629, Valletta, Malta, ELRA, 2010.	2-clause BSD	%	%	%
Liner2 with TIMEX model		Kocoń J., Marcińczuk M. Liner2.5 model Timex, CLARIN-PL digital repository, 2016.		%	%	%

Sentiment analysis

System name and URL	Approach	Main publication	License	P	R	F
Sentipejd				%	%	%

Mention detection

Precision, recall and F-measure are calculated on Polish Coreference Corpus data with two alternative mention detection scores:

EXACT: score of exact boundary matches (an automatic and a manual mention match if they have exactly the same boundaries; in other words, they consist of the same tokens)
HEAD: score of head matches (we reduce the automatic and the manual mentions to their single head tokens and compare them).

System name and URL	Approach	Main publication	License	EXACT			HEAD
System name and URL	Approach	Main publication	License	P	R	F	P	R	F
Mention Detector	Collects mention candidates from available sources – morphosyntactical, shallow parsing, named entity and/or zero anaphora detection tools	Ogrodniczuk M., Głowińska K., Kopeć M., Savary A., Zawisławska M. Coreference in Polish: Annotation, Resolution and Evaluation, chapter 10.6. Walter De Gruyter, 2015.	CC BY 3	66.79%	67.21%	67.00%	88.29%	89.41%	88.85%

Coreference resolution

As there is still no consensus about the single best coreference resolution metrics, CoNLL measure is used (average of MUC, B3 and CEAFE F-measure values). For end-to-end systems CoNLL-2011 shared task-based approach is used, so two result calculation strategies are presented:

INTERSECT: consider only correct system mentions (i.e. the intersection between gold and system mentions)
TRANSFORM: unify system and gold mention sets using the following procedure for twinless mentions (without a corresponding mention in the second set):
1. insert twinless gold mentions into system mention set as singletons
2. remove twinless singleton system mentions
3. insert twinless non-singletion system mentions into gold set as singletons.

The results are produced on Polish Coreference Corpus data.

System name and URL	Approach	Main publication	License	GOLD	EXACT INTERSECT	EXACT TRANSFORM	HEAD INTERSECT	HEAD TRANSFORM
Ruler	Rule-based	Ogrodniczuk M., Kopeć M. End-to-end coreference resolution baseline system for Polish. In Z. Vetulani (ed.), Proceedings of the 5th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, pp. 167–171, Poznań, Poland, 2011.	CC BY 3	73.40%	78.54%	66.55%	76.27%	70.11%
Bartek3	Statistical	Kopeć M., Ogrodniczuk M. Creating a Coreference Resolution System for Polish. In Proceedings of the 8th International Conference on Language Resources and Evaluation, LREC 2012, pp. 192–195, ELRA.	CC BY 3	78.41%	80.86%	68.96%	78.58%	72.15%
BartekS1	Sieve-based	Nitoń B., Ogrodniczuk M. Multi-Pass Sieve Coreference Resolution System for Polish. In J. Gracia, F. Bond, J. P. McCrae, P. Buitelaar, Ch. Chiarcos, S. Hellmann (eds.) Proceedings of the 1st Conference on Language, Data and Knowledge (LDK 2017), Galway, Ireland, 19–20 June 2017.	CC BY 3	80.47%	?	?	?	?

Extractive summarization

The table presents results of evaluation on the Polish Summaries Corpus (abstractive summaries, 20% length of the original document). ROUGE covers several metrics, with the following variants used here:

ROUGE n which counts n-gram co-occurrences between reference (gold) summaries and system summary
ROUGE Mn with ROUGE score calculated for each text using a single manual summary which gives the highest score as a reference.

System name and URL	Approach	Main publication	License	ROUGE 1	ROUGE 2	ROUGE 3	ROUGE M1	ROUGE M2	ROUGE M3
PolSum	?	Ciura M., Grund D., Kulików S., Suszczańska N. A System to Adapt Techniques of Text Summarizing to Polish. In Okatan A. (ed.) International Conference on Computational Intelligence, pp. 117–120, Istanbul, Turkey. International Computational Intelligence Society, 2004.	?	? %	? %	? %	? %	? %	? %
Lakon	sentence extraction relying on one of: positional heuristics, word frequency features, lexical chains information	Dudczak A. Zastosowanie wybranych metod eksploracji danych do tworzenia streszczeń tekstów prasowych dla języka polskiego. MSc thesis, Poznań Technical University, 2007.	3-clause BSD	55.4%	20.8%	14.4%	62.9%	33.3%	27.4%
Summarizer	sentence extraction with machine learning	Świetlicka J. Metody maszynowego uczenia w automatycznym streszczaniu tekstów. MSc thesis, Warsaw University 2010.	GPL 3	58.0%	22.6%	16.1%	65.4%	35.8%	29.8%
Open Text Summarizer	word frequency based sentence extraction	Rotem N. Open Text Summarizer. 2003.	GPL?	51.3%	13.6%	9.0%	58.5%	22.5%	17.9%
Emily-C	?	?	?	52.9%	15.1%	10.0%	58.8%	24.2%	19.2%
Emily-S	?	?	?	53.0%	15.4%	10.4%	59.4%	24.7%	20.1%
Nicolas	?	?	CC BY 3	58.1%	23.8%	17.1%	66.4%	38.4%	32.7%

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-Here we will document performance of various NLP systems for Polish.
+This page documents performance of most popular contemporary NLP systems for Polish.
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+Line 6:
-==Test collections==
* '''Performance measure:''' per token accuracy. (The convention is for this to be measured on all tokens, including punctuation tokens and other unambiguous tokens.)
* '''English'''
** '''Penn Treebank''' ''Wall Street Journal'' (WSJ) release 3 (LDC99T42). The splits of data for this task were not standardized early on (unlike for parsing) and early work uses various data splits defined by counts of tokens or by sections.  Most work from 2002 on adopts the following data splits, introduced by Collins (2002):
*** '''Training data:''' sections 0-18
*** '''Development test data:''' sections 19-21
*** '''Testing data:''' sections 22-24
+== Single-word lemmatization and morphological analysis ==
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+Line 8:
-* '''French'''
** '''French TreeBank''' (FTB, Abeillé et al; 2003) ''Le Monde'', December 2007 version, 28-tag tagset (CC tagset, Crabbé and Candito, 2008). Classical data split (10-10-80):
*** '''Training data:''' sentences 2471 to 12351
*** '''Development test data:''' sentences 1236 to 2470
*** '''Testing data:''' sentences 1 to 1235
+|| '''System name and URL'''                || '''Approach''' || '''Main publication''' || '''License''' || '''P''' || '''R''' || '''F''' ||
|| [[http://sgjp.pl/morfeusz/|Morfeusz]]   ||     || Woliński, M. [[http://nlp.ipipan.waw.pl/Bib/woli:06.pdf|Morfeusz — a practical tool for the morphological analysis of Polish]]. In M.A. Kłopotek, S.T. Wierzchoń, K. Trojanowski (eds.) Proceedings of the International IIS:IIPWM 2006 Conference, pp. 511–520, Wisła, Poland, 2006. || 2-clause BSD || %  || % || % ||
|| [[https://github.com/morfologik/|Morfologik]] ||     || Miłkowski M. [[http://doi.wiley.com/10.1002/spe.971|Developing an open-source, rule-based proofreading tool]]. Software: Practice and Experience, 40(7):543–566, 2010. ||  || %  || % || % ||
|| [[http://zil.ipipan.waw.pl/LemmaPL|LemmaPL]] || dictionary-based rules and heuristics || Kobyliński Ł. (unpublished) || GPL || % || % || % ||
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-== Tables of results ==
+== Multi-word lemmatization ==
-Line 22:
+Line 16:
-===WSJ===

{| border="1" cellpadding="5" cellspacing="1" width="100%"
|-
! System name
! Short description
! Main publication
! Software
! Extra Data?***
! All tokens
! Unknown words
! License
|-
| TnT*
| Hidden markov model
| Brants (2000)
| [http://www.coli.uni-saarland.de/~thorsten/tnt/ TnT]
| No
| 96.46%
| 85.86%
| Academic/research use only ([http://www.coli.uni-saarland.de/~thorsten/tnt/tnt-license.html license])
|-
| MElt
| MEMM with external lexical information
| Denis and Sagot (2009)
| [https://gforge.inria.fr/projects/lingwb/ Alpage linguistic workbench]
| No
| 96.96%
| 91.29%
| CeCILL-C
|-
| GENiA Tagger**
| Maximum entropy cyclic dependency network
| Tsuruoka, et al (2005)
| [http://www.nactem.ac.uk/tsujii/GENIA/tagger/ GENiA]
| No
| 97.05%
| Not available
| Gratis for non-commercial usage
|-
| Averaged Perceptron
| Averaged Perception discriminative sequence model
| Collins (2002)
| Not available
| No
| 97.11%
| Not available
| Unknown
|-
| Maxent easiest-first
| Maximum entropy bidirectional easiest-first inference
| Tsuruoka and Tsujii (2005)
| [http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/postagger/ Easiest-first]
| No
| 97.15%
| Not available
| Unknown
|-
| SVMTool
| SVM-based tagger and tagger generator
| Giménez and Márquez (2004)
| [http://www.lsi.upc.es/~nlp/SVMTool/ SVMTool]
| No
| 97.16%
| 89.01%
| LGPL 2.1
|-
| LAPOS
| Perceptron based training with lookahead
| Tsuruoka, Miyao, and Kazama (2011)
| [http://www.logos.t.u-tokyo.ac.jp/~tsuruoka/lapos/ LAPOS]
| No
| 97.22%
| Not available
| MIT
|-
| Morče/COMPOST
| Averaged Perceptron
| Spoustová et al. (2009)
| [http://ufal.mff.cuni.cz/compost COMPOST]
| No
| 97.23%
| Not available
| Non-free ([http://ufal.mff.cuni.cz/compost/register.php academic-only])
|-
| Morče/COMPOST
| Averaged Perceptron
| Spoustová et al. (2009)
| [http://ufal.mff.cuni.cz/compost COMPOST]
| Yes
| 97.44%
| Not available
| Unknown
|-
| Stanford Tagger 1.0
| Maximum entropy cyclic dependency network
| Toutanova et al. (2003)
| [http://nlp.stanford.edu/software/tagger.shtml Stanford Tagger]
| No
| 97.24%
| 89.04%
| GPL v2+
|-
| Stanford Tagger 2.0
| Maximum entropy cyclic dependency network
| Manning (2011)
| [http://nlp.stanford.edu/software/tagger.shtml Stanford Tagger]
| No
| 97.29%
| 89.70%
| GPL v2+
|-
| Stanford Tagger 2.0
| Maximum entropy cyclic dependency network
| Manning (2011)
| [http://nlp.stanford.edu/software/tagger.shtml Stanford Tagger]
| Yes
| 97.32%
| 90.79%
| GPL v2+
|-
| LTAG-spinal
| Bidirectional perceptron learning
| Shen et al. (2007)
| [http://www.cis.upenn.edu/~xtag/spinal/ LTAG-spinal]
| No
| 97.33%
| Not available
| Unknown
|-
| SCCN
| Semi-supervised condensed nearest neighbor
| Søgaard (2011)
| [http://cst.dk/anders/scnn/ SCCN]
| Yes
| 97.50%
| Not available
| Unknown
|-
| CharWNN
| MLP with Neural Character Embeddings
| dos Santos and Zadrozny (2014)
| Not available
| No
| 97.32%
| 89.86%
| Unknown
|-
| structReg
| CRFs with structure regularization
| Sun(2014)
| Not available
| No
| 97.36%
| Not available
| Unknown
|-
| BI-LSTM-CRF
| Bidirectional LSTM-CRF Model
| Huang et al. (2015)
| Not available
| No
| 97.55%
| Not available
| Unknown
|-
| NLP4J
| Dynamic Feature Induction
| Choi (2016)
| [https://github.com/emorynlp/nlp4j NLP4J]
| Yes
| 97.64%
| 92.03%
| Apache 2
|}

(*) TnT: Accuracy is as reported by Giménez and Márquez (2004) for the given test collection. Brants (2000) reports 96.7% token accuracy and 85.5% unknown word accuracy on a 10-fold cross-validation of the Penn WSJ corpus.

(**) GENiA: Results are for models trained and tested on the given corpora (to be comparable to other results).  The distributed GENiA tagger is trained on a mixed training corpus and gets 96.94% on WSJ, and 98.26% on GENiA biomedical English.

(***) Extra data: Whether system training exploited (usually large amounts of) extra unlabeled text, such as by semi-supervised learning, self-training, or using distributional similarity features, beyond the standard supervised training data.

===FTB===

{| border="1" cellpadding="5" cellspacing="1" width="100%"
|-
! System name
! Short description
! Main publication
! Software
! Extra Data?***
! All tokens
! Unknown words
! License
|-
| Morfette
| Perceptron with external lexical information*
| Chrupała et al. (2008), Seddah et al. (2010)
| [http://sites.google.com/site/morfetteweb/ Morfette]
| No
| 97.68%
| 90.52%
| New BSD
|-
| SEM
| CRF with external lexical information*
| Constant et al. (2011)
| [http://www.univ-orleans.fr/lifo/Members/Isabelle.Tellier/SEM.html SEM]
| No
| 97.7%
| Not available
| "GNU"(?)
|-
| MElt
| MEMM with external lexical information*
| Denis and Sagot (2009)
| [https://gforge.inria.fr/projects/lingwb/ Alpage linguistic workbench]
| No
| 97.80%
| 91.77%
| CeCILL-C
|}

(*) External lexical information from the Lefff lexicon (Sagot 2010, [https://gforge.inria.fr/frs/?group_id=482 Alexina project])

== References ==

* Brants, Thorsten. 2000. [http://acl.ldc.upenn.edu/A/A00/A00-1031.pdf TnT -- A Statistical Part-of-Speech Tagger]. "6th Applied Natural Language Processing Conference".

* Chrupała, Grzegorz, Dinu, Georgiana and van Genabith, Josef. 2008. [http://www.lrec-conf.org/proceedings/lrec2008/pdf/594_paper.pdf Learning Morphology with Morfette]. "LREC 2008". 

* Collins, Michael. 2002. [http://people.csail.mit.edu/mcollins/papers/tagperc.pdf Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms]. ''EMNLP 2002''.

* Constant, Matthieu, Tellier, Isabelle, Duchier, Denys, Dupont, Yoann, Sigogne, Anthony, and Billot, Sylvie. [http://www.lirmm.fr/~lopez/TALN2011/Longs-TALN+RECITAL/Tellier_taln11_submission_54.pdf Intégrer des connaissances linguistiques dans un CRF : application à l'apprentissage d'un segmenteur-étiqueteur du français]. "TALN'11"

* Denis, Pascal and Sagot, Benoît. 2009. [http://alpage.inria.fr/~sagot/pub/paclic09tagging.pdf Coupling an annotated corpus and a morphosyntactic lexicon for state-of-the-art POS tagging with less human effort]. "PACLIC 2009"

* Giménez, J., and Márquez, L. 2004. [http://www.lsi.upc.es/~nlp/SVMTool/lrec2004-gm.pdf SVMTool: A general POS tagger generator based on Support Vector Machines]. ''Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC'04)''. Lisbon, Portugal. 

* Manning, Christopher D. 2011. Part-of-Speech Tagging from 97% to 100%: Is It Time for Some Linguistics? In Alexander Gelbukh (ed.), Computational Linguistics and Intelligent Text Processing, 12th International Conference, CICLing 2011, Proceedings, Part I. Lecture Notes in Computer Science 6608, pp. 171--189. Springer.

* Seddah, Djamé, Chrupała, Grzegorz, Çetinoglu, Özlem and Candito, Marie. 2010. [http://aclweb.org/anthology-new/W/W10/W10-1410.pdf Lemmatization and Lexicalized Statistical Parsing of Morphologically Rich Languages: the Case of French] "SPMRL 2010 (NAACL 2010 workshop)"

* Shen, L., Satta, G., and  Joshi, A. 2007. [http://acl.ldc.upenn.edu/P/P07/P07-1096.pdf Guided learning for bidirectional sequence classification]. ''Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (ACL 2007)'', pages 760-767.

* Søgaard, Anders. 2011. Semi-supervised condensed nearest neighbor for part-of-speech tagging. The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT). Portland, Oregon.

* Spoustová, Drahomíra "Johanka", Jan Hajič, Jan Raab and Miroslav Spousta. 2009. Semi-supervised Training for the Averaged Perceptron POS Tagger. Proceedings of the 12 EACL, pages 763-771.

* Toutanova, K., Klein, D., Manning, C.D., Yoram Singer, Y. 2003. [http://nlp.stanford.edu/kristina/papers/tagging.pdf Feature-rich part-of-speech tagging with a cyclic dependency network]. ''Proceedings of HLT-NAACL 2003'', pages 252-259.

* Tsuruoka, Yoshimasa, Yuka Tateishi, Jin-Dong Kim, Tomoko Ohta, John McNaught, Sophia Ananiadou, and Jun'ichi Tsujii. 2005. "[http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/papers/pci05.pdf Developing a Robust Part-of-Speech Tagger for Biomedical Text, Advances in Informatics]" - ''10th Panhellenic Conference on Informatics'', '''LNCS 3746''', pp. 382-392, 2005 

* Tsuruoka, Yoshimasa, Yusuke Miyao, and Jun’ichi Kazama. 2011. "[http://aclweb.org/anthology-new/W/W11/W11-0328.pdf Learning with Lookahead: Can History-Based Models Rival Globally Optimized Models?]" ''Proceedings of the Fifteenth Conference on Computational Natural Language Learning'', pp 238–246, 2011.

* Tsuruoka, Yoshimasa and Jun'ichi Tsujii. 2005. "[http://www-tsujii.is.s.u-tokyo.ac.jp/~tsuruoka/papers/emnlp05bidir.pdf Bidirectional Inference with the Easiest-First Strategy for Tagging Sequence Data]", ''Proceedings of HLT/EMNLP 2005'', pp. 467-474.

* Sun, Xu. "[http://papers.nips.cc/paper/5643-structure-regularization-for-structured-prediction.pdf Structure Regularization for Structured Prediction]". ''In Neural Information Processing Systems (NIPS)''. 2402-2410. 2014

* Cicero dos Santos, and Bianca Zadrozny. "[http://jmlr.org/proceedings/papers/v32/santos14.pdf Learning character-level representations for part-of-speech tagging]". ''In Proceedings of the 31st International Conference on Machine Learning, JMLR: W&CP volume 32''. 2014.

* Z. H. Huang, W. Xu, and K. Yu. "[http://arxiv.org/abs/1508.01991 Bidirectional LSTM-CRF Models for Sequence Tagging]". ''In arXiv:1508.01991''. 2015.

* Jinho D. Choi. 2016. "[https://aclweb.org/anthology/N/N16/N16-1031.pdf Dynamic Feature Induction: The Last Gist to the State-of-the-Art]", Proceedings of NAACL 2016.

== See also ==
* [[POS Induction (State of the art)]]
* [[Part-of-speech tagging]]
* [[State of the art]]
+|| '''System name and URL'''                || '''Approach''' || '''Main publication''' || '''License''' || '''Accuracy''' ||
|| –   || rule-based || Degórski, Ł. [[http://nlp.ipipan.waw.pl/Bib/deg:11.pdf|Towards the lemmatisation of Polish nominal syntactic groups using a shallow grammar]]. In P. Bouvry, M.A. Kłopotek, F. Leprevost, M. Marciniak, A. Mykowiecka, H. Rybiński (eds.) Security and Intelligent Information Systems, Lecture Notes in Computer Science vol. 7053, pp. 370–378, Springer-Verlag Berlin Heidelberg, 2012. || ? || 82.90% ||
|| –   || automatic generation of lemmatization rules using CRF    || Radziszewski A. [[http://aclweb.org/anthology/P/P13/P13-1069.pdf|Learning to lemmatise Polish noun phrases]]. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Volume 1: Long Papers. ACL, pp. 701–709. || GPL || 80.70%  ||
|| –   || automatic generation of lemmatization rules from a corpus || Abramowicz W., Filipowska A., Małyszko J., Wagner T. [[http://ltc.amu.edu.pl/a2015/book/papers/MWE-1.pdf|Lemmatization of Multi-Word Entity Names for Polish Language Using Rules Automatically Generated Based on the Corpus Analysis]]. In Z. Vetulani, J. Mariani (eds.) Human Language Technologies as a Challenge for Computer Science and Linguistics, Fundacja Uniwersytetu im. A. Mickiewicza, pp. 540–544, Poznań 2015. || ? || 82.10%  ||
|| PoLem || dictionary-based rules and heuristics || Marcińczuk M. (in print) || GPL || 97.99%  ||
-Line 293:
+Line 23:
-[[Category:State of the art]]
+== Disambiguated POS tagging ==

The comparisons are performed using plain text as input and reporting the accuracy lower bound (Acc,,lower,,) metric proposed by [[http://www.plwordnet.pwr.wroc.pl/redmine/attachments/download/669/taggereval.pdf|Radziszewski and Acedański (2012)]]. The metric penalizes all segmentation changes in regard to the gold standard and treats such tokens as misclassified. Furthermore, we report separate metric values for both known and unknown words to assess the performance of guesser modules built into the taggers. These are indicated as Acc^K^,,lower,, for known and Acc^U^,,lower,, for unknown words.

The experiments have been performed on the manually annotated part of the [[http://www.nkjp.pl|National Corpus of Polish]] v. 1.1 (1M tokens). The ten-fold cross-validation procedure has been followed, by re-evaluating the methods ten times, each time selecting one of ten parts of the corpus for testing and the remaining parts for training the taggers. The provided results are averages calculated over ten training and testing sequences. Each of the taggers and each tagger ensemble has been trained and tested on the same set of cross-validation folds, so the results are directly comparable. Each of the training folds has been reanalyzed, according to the procedure described in ([[http://nlp.pwr.wroc.pl/ltg/files/publications/wcrft.pdf|A Tiered CRF Tagger for Polish|Radziszewski 2013)]], using the Maca toolkit ([[http://mt-archive.info/FreeRBMT-2011-Radziszewski.pdf|Radziszewski and Śniatowski 2011)]]. The idea of a morphological reanalysis of the gold-standard data is to allow the trained tagger to see similar input that is expected in the tagging phase. The training data is firstly turned into plain text and analyzed using the same mechanism that will be used by the tagger during actual tagging process. The output of the analyzer is then synchronized with the original gold-standard data, by using the original tokenization. Tokens with changed segmentation are taken from the gold-standard intact. In the case of tokens for which the segmentation did not change in the process of morphological analysis, the produced interpretations are compared with the original. A token is marked as an unknown word if the correct interpretation has not been produced by the analyzer. Maca has been run with the morfeusz-nkjp-official configuration, which uses Morfeusz SGJP analyzer ([[http://nlp.ipipan.waw.pl/Bib/woli:06.pdf|Woliński 2006]]) and no guesser module.

Tagger efficiency was compared by measuring training and tagging times of each of the methods on the same machine. 1.1M token set was used both for training and tagging stages. The total processing time included model loading/saving time and other I/O operations (e.g. reading/writing the tokens).

|| '''System name and URL'''                      || '''Approach''' || '''Main publication''' || '''License''' || '''Acc,,lower,,''' || '''Acc^K^,,lower,,''' || '''Acc^U^,,lower,,''' ||'''Training time''' ||'''Tagging time''' ||
|| [[http://zil.ipipan.waw.pl/OpenNLP|OpenNLP]]   || !MaxEnt model      || Kobyliński Ł., Kieraś W. [[attachment:kob-kie-16.pdf|Part of Speech Tagging for Polish: State of the Art and Future Perspectives]]. In Proceedings of the 17th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2016), Konya, Turkey, 2016. || GPL || 87.24%  || 88.02% || 62.05% ||<)> 11095 s ||<)> 362 s ||
|| [[http://zil.ipipan.waw.pl/PANTERA|Pantera]]   || rule-based adapted Brill tagger      || Acedański S. [[http://ripper.dasie.mimuw.edu.pl/~accek/homepage/wp-content/papercite-data/pdf/ace10.pdf|A Morphosyntactic Brill Tagger for Inflectional Languages]]. In H. Loftsson, E. Rögnvaldsson, S. Helgadóttir (eds.) Advances in Natural Language Processing, LNCS 6233, pp. 3–14, Springer, 2010. || GPL 3 || 88.95%  || 91.22% || 15.19% ||<)> 2624 s ||<)> 186 s ||
|| [[http://nlp.pwr.wroc.pl/redmine/projects/wmbt/wiki|WMBT]]         || memory-based      || Radziszewski A., Śniatowski T. [[http://nlp.pwr.wroc.pl/redmine/attachments/download/420/wmbt.pdf|A memory-based tagger for Polish]]. In: Z. Vetulani (ed.) Proceedings of the 5th Language and Technology Conference (LTC 2011), pp. 556–560, Poznań, Poland. ||  || 90.33% || 91.26% || 60.25% ||<)> 548 s ||<)> 4338 s ||
|| [[http://nlp.pwr.wroc.pl/redmine/projects/wcrft/wiki|WCRFT]]       || tiered, CRF-based || Radziszewski A. [[https://pdfs.semanticscholar.org/719c/0b314bc4ac5204f8d288a8c4e6053f08285d.pdf|A Tiered CRF Tagger for Polish]]. In R. Bembenik, Ł. Skonieczny, H. Rybiński, M. Kryszkiewicz, M. Niezgódka (eds.) Intelligent Tools for Building a Scientific Information Platform, pp. 215–230, Springer Berlin Heidelberg, 2013. || LGPL 3.0 || 90.76%  || 91.92% || 53.18% ||<)> 27242 s ||<)> 420 s ||
|| [[http://zil.ipipan.waw.pl/Concraft|Concraft]] || mutually dependent CRF layers    || Waszczuk J. [[http://www.aclweb.org/anthology/C12-1170|Harnessing the CRF complexity with domain-specific constraints. The case of morphosyntactic tagging of a highly inflected language]]. In Proceedings of the 24th International Conference on Computational Linguistics (COLING 2012), pp. 2789–2804, Mumbai, India, 2012. || 2-clause BSD || 91.07%  || 92.06% || 58.81% ||<)> 26675 s ||<)> 403 s ||
|| [[http://zil.ipipan.waw.pl/PoliTa|PoliTa]]     || voting ensemble    || Kobyliński Ł. [[http://www.lrec-conf.org/proceedings/lrec2014/pdf/1018_Paper.pdf|PoliTa: A multitagger for Polish]]. In N. Calzolari, K. Choukri, T. Declerck, H. Loftsson, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, S. Piperidis (eds.) Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014), pp. 2949–2954, Reykjavík, Iceland, ELRA, 2014. || GPL || 92.01%  || 92.91% || 62.81% ||<:> N/A ||<:> N/A ||


== Dependency parsing ==

10-fold cross-validation was used to test the quality of !MaltParser and !MateParser on Polish data (average LAS and UAS scores of 10 tests are provided).

|| '''System name and URL'''                       || '''Approach''' || '''Main publication''' || '''License''' || '''LAS''' || '''UAS''' ||
|| [[http://zil.ipipan.waw.pl/PolishDependencyParser|Polish Dependency parser (MaltParser)]] || trained on the extended version of the [[http://zil.ipipan.waw.pl/Sk%C5%82adnica|Polish dependency treebank]] with !MaltParser  ||<rowspan=2> Wróblewska A. ''Polish Dependency Parser Trained on an Automatically Induced Dependency Bank''. PhD dissertation, Institute of Computer Science, Polish Academy of Sciences, Warsaw, 2014. ||  GPL 3|| 84%  || 89% ||
|| [[http://zil.ipipan.waw.pl/PolishDependencyParser|Polish Dependency parser (MateParser)]] || trained on the same data with !MateParser  ||  GPL 3 || 89%  || 93% ||
|| [[http://zil.ipipan.waw.pl/|Google parser]]     ||     || ||  || %  || % ||


== Shallow parsing ==

|| '''System name and URL'''                          || '''Approach''' || '''Main publication''' || '''License''' || '''P''' || '''R''' || '''F''' ||
|| [[http://zil.ipipan.waw.pl/Spejd|Spejd]] || rule-based || Buczyński A., Przepiórkowski A. [[http://nlp.ipipan.waw.pl/~adamp/Papers/2007-ltc-spade/Spade.pdf|Spejd: A shallow processing and morphological disambiguation tool]]. In Z. Vetulani, H. Uszkoreit (eds.) Human Language Technology: Challenges of the Information Society, LNCS 5603, pp. 131–141. Springer-Verlag, Berlin, 2009. || GPL 3 || %  || % || % ||


== Deep parsing ==

|| '''System name and URL'''                  || '''Approach''' || '''Main publication''' || '''License''' || '''P''' || '''R''' || '''F''' ||
|| [[http://zil.ipipan.waw.pl/Świgra|Świgra]] ||     || ||  || %  || % || % ||
|| [[http://zil.ipipan.waw.pl/LFG|POLFIE]]    ||     || || GPL 3 (grammar) || %  || % || % ||
|| [[http://zil.ipipan.waw.pl/ENIAM|ENIAM]]   ||     || || GPL 3 || %  || % || % ||


== Word sense disambiguation ==

Manually annotated subcorpus of the National Corpus of Polish with 3889 texts and 1217822 segments was used for training and test (cross-validation); it contained 34186 occurrences of multi-sense words (1–7 senses). Simple heuristics selecting the most frequent sense resulted in 78.3% accuracy. The table presents results of leave-one-out evaluation with individual selection of each ambiguous words (as described in the publication).

|| '''System name and URL'''                || '''Approach'''   || '''Main publication''' || '''License''' || '''Accuracy''' ||
|| [[http://zil.ipipan.waw.pl/WSDDE|WSDDE]] || Machine-learning || Kopeć M., Młodzki R., Przepiórkowski A. [[http://nkjp.pl/settings/papers/NKJP_ksiazka.pdf|Automatyczne znakowanie sensami słów]]. In A. Przepiórkowski, M. Bańko, R.L. Górski, B. Lewandowska-Tomaszczyk (eds.) Narodowy Korpus Języka Polskiego, pp. 209–224. Wydawnictwo Naukowe PWN, Warsaw, 2012.  || GPL 3 || 91.46%  ||
|| [[http://babelfy.org/|Babelfy]] || !BabelNet-powered || ?  || ? || ?  ||


== Named entity recognition ==

|| '''System name and URL'''           || '''Approach''' || '''Main publication''' || '''License''' || '''P''' || '''R''' || '''F''' ||
|| [[http://zil.ipipan.waw.pl/Nerf|NERF]] linear-chain CRF ||  || Savary A., Waszczuk J., Przepiórkowski A. [[http://nlp.ipipan.waw.pl/~adamp/Papers/2010-lrec-as/sav-wasz-przep-lrec-2010.pdf|Towards the annotation of named entities in the National Corpus of Polish]]. In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC 2010), pp. 3622–3629, Valletta, Malta, ELRA, 2010.  || 2-clause BSD || %  || % || % ||
|| [[http://nlp.pwr.wroc.pl/narzedzia-i-zasoby/narzedzia/liner2|Liner2]] with [[https://clarin-pl.eu/dspace/handle/11321/302|TIMEX model]] || || Kocoń J., Marcińczuk M. [[http://hdl.handle.net/11321/302|Liner2.5 model Timex]], CLARIN-PL digital repository, 2016. ||  || %  || % || % ||


== Sentiment analysis ==

|| '''System name and URL'''                          || '''Approach''' || '''Main publication''' || '''License''' || '''P''' || '''R''' || '''F''' ||
|| [[http://zil.ipipan.waw.pl/Sentipejd|Sentipejd]]   ||     || ||  || %  || % || % ||


== Mention detection ==

Precision, recall and F-measure are calculated on [[http://clip.ipipan.waw.pl/PCC|Polish Coreference Corpus]] data with two alternative mention detection scores:
 * EXACT: score of exact boundary matches (an automatic and a manual mention match if they have exactly the same boundaries; in other words, they consist of the same tokens)
 * HEAD: score of head matches (we reduce the automatic and the manual mentions to their single head tokens and compare them).

||<|2> '''System name and URL''' ||<|2> '''Approach''' ||<|2> '''Main publication''' ||<|2> '''License''' |||||| '''EXACT''' |||||| '''HEAD''' ||
||<:> '''P''' ||<:> '''R''' ||<:> '''F''' ||<:> '''P''' ||<:> '''R''' ||<:> '''F''' ||
|| [[http://zil.ipipan.waw.pl/MentionDetector|Mention Detector]]   || Collects mention candidates from available sources – morphosyntactical, shallow parsing, named entity and/or zero anaphora detection tools || Ogrodniczuk M., Głowińska K., Kopeć M., Savary A., Zawisławska M. [[http://www.degruyter.com/view/product/428667|Coreference in Polish: Annotation, Resolution and Evaluation]], chapter 10.6. Walter De Gruyter, 2015.  ||  CC BY 3  || 66.79% || 67.21% || 67.00% || 88.29% || 89.41% || 88.85% ||


== Coreference resolution ==


As there is still no consensus about the single best coreference resolution metrics, CoNLL measure is used (average of MUC, B3 and CEAFE F-measure values). For end-to-end systems CoNLL-2011 shared task-based approach is used, so two result calculation strategies are presented:
 * INTERSECT: consider only correct system mentions (i.e. the intersection between gold and system mentions)
 * TRANSFORM: unify system and gold mention sets using the following procedure for twinless mentions (without a corresponding mention in the second set):
  1. insert twinless gold mentions into system mention set as singletons
  1. remove twinless singleton system mentions
  1. insert twinless non-singletion system mentions into gold set as singletons.

The results are produced on [[http://clip.ipipan.waw.pl/PCC|Polish Coreference Corpus]] data.

|| '''System name and URL'''                          || '''Approach''' || '''Main publication''' || '''License''' || '''GOLD''' || '''EXACT INTERSECT''' || '''EXACT TRANSFORM''' || '''HEAD INTERSECT''' || '''HEAD TRANSFORM''' ||
|| [[http://zil.ipipan.waw.pl/Ruler|Ruler]]           || Rule-based     || Ogrodniczuk M., Kopeć M. [[http://nlp.ipipan.waw.pl/Bib/ogro:kop:11:ltc.pdf|End-to-end coreference resolution baseline system for Polish]]. In Z. Vetulani (ed.), Proceedings of the 5th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, pp. 167–171, Poznań, Poland, 2011.  || CC BY 3 || 73.40%  || 78.54% || 66.55% || 76.27% || 70.11% ||
|| [[http://zil.ipipan.waw.pl/Bartek|Bartek3]]        || Statistical    || Kopeć M., Ogrodniczuk M. [[http://www.lrec-conf.org/proceedings/lrec2012/pdf/1064_Paper.pdf|Creating a Coreference Resolution System for Polish]]. In Proceedings of the 8th International Conference on Language Resources and Evaluation, LREC 2012, pp. 192–195, ELRA. || CC BY 3 || 78.41% ||  80.86% || 68.96% || 78.58% || 72.15% ||
|| [[http://zil.ipipan.waw.pl/Bartek|BartekS1]]       || Sieve-based    || Nitoń B., Ogrodniczuk M. Multi-Pass Sieve Coreference Resolution System for Polish. In J. Gracia, F. Bond, J. P. !McCrae, P. Buitelaar, Ch. Chiarcos, S. Hellmann (eds.) Proceedings of the 1st Conference on Language, Data and Knowledge (LDK 2017), Galway, Ireland, 19–20 June 2017. || CC BY 3 || 80.47% ||  ? || ? || ? || ? ||


== Extractive summarization ==

The table presents results of evaluation on the [[http://zil.ipipan.waw.pl/PolishSummariesCorpus|Polish Summaries Corpus]] (abstractive summaries, 20% length of the original document). [[http://anthology.aclweb.org/W/W04/W04-1013.pdf|ROUGE]] covers several metrics, with the following variants used here:
 * ROUGE n which counts n-gram co-occurrences between reference (gold) summaries and system summary
 * ROUGE Mn with ROUGE score calculated for each text using a single manual summary which gives the highest score as a reference.

|| '''System name and URL'''                          || '''Approach''' || '''Main publication''' || '''License''' || '''ROUGE 1''' || '''ROUGE 2''' || '''ROUGE 3''' || '''ROUGE M1''' || '''ROUGE M2''' || '''ROUGE M3''' ||
|| [[http://las.aei.polsl.pl/PolSum/#/Home|PolSum]]   || ? || Ciura M., Grund D., Kulików S., Suszczańska N. [[http://sun.aei.polsl.pl/~mciura/publikacje/summarizing.pdf|A System to Adapt Techniques of Text Summarizing to Polish]]. In Okatan A. (ed.) International Conference on Computational Intelligence, pp. 117–120, Istanbul, Turkey. International Computational Intelligence Society, 2004.  || ? || ? % || ? % || ? % || ? % || ? % || ? % ||
|| [[http://www.cs.put.poznan.pl/dweiss/research/lakon/|Lakon]] || sentence extraction relying on one of: positional heuristics, word frequency features, lexical chains information || Dudczak A. [[http://www.cs.put.poznan.pl/dweiss/research/lakon/publications/thesis.pdf|Zastosowanie wybranych metod eksploracji danych do tworzenia streszczeń tekstów prasowych dla języka polskiego]]. MSc thesis, Poznań Technical University, 2007. || 3-clause BSD || 55.4% || 20.8% || 14.4% || 62.9% || 33.3% || 27.4% ||
|| [[http://clip.ipipan.waw.pl/Summarizer|Summarizer]] || sentence extraction with machine learning || Świetlicka J. [[http://nlp.ipipan.waw.pl/~adamp/msc/swietlicka.joanna/TekstPracy.pdf.gz|Metody maszynowego uczenia w automatycznym streszczaniu tekstów]]. MSc thesis, Warsaw University 2010. || GPL 3 || 58.0% || 22.6% || 16.1% || 65.4% || 35.8% || 29.8% ||
|| [[https://www.splitbrain.org/services/ots|Open Text Summarizer]]   || word frequency based sentence extraction || Rotem N. [[https://github.com/neopunisher/Open-Text-Summarizer|Open Text Summarizer]]. 2003. || GPL? || 51.3% || 13.6% || 9.0% || 58.5% || 22.5% || 17.9% ||
|| Emily-C   ||  ?  || ? || ? || 52.9% || 15.1% || 10.0% || 58.8% || 24.2% || 19.2% ||
|| Emily-S   ||  ?  || ? || ? || 53.0% || 15.4% || 10.4% || 59.4% || 24.7% || 20.1% ||
|| [[http://zil.ipipan.waw.pl/Nicolas|Nicolas]] || ? || ? || CC BY 3 || 58.1% || 23.8% || 17.1% || 66.4% || 38.4% || 32.7% ||

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