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benchmarks

Benchmarks

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

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

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Morfologik

Miłkowski M. Developing an open-source, rule-based proofreading tool. Software: Practice and Experience, 40(7):543–566, 2010.

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Disambiguated POS tagging

The comparisons are performed using plain text as input and reporting the accuracy lower bound (Acclower) 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 AccKlower for known and AccUlower 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

Acclower

AccKlower

AccUlower

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

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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

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Deep parsing

System name and URL

Approach

Main publication

License

P

R

F

Świgra

%

%

%

POLFIE

GPL 3 (grammar)

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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

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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

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%

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Liner2 with TIMEX model

Kocoń J., Marcińczuk M. Liner2.5 model Timex, CLARIN-PL digital repository, 2016.

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%

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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

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%

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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.

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? %

? %

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

?

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?

53.0%

15.4%

10.4%

59.4%

24.7%

20.1%

Nicolas

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CC BY 3

58.1%

23.8%

17.1%

66.4%

38.4%

32.7%