Polish Coreference Corpus

Ogrodniczuk, Maciej; Głowińska, Katarzyna; Kopeć, Mateusz; Savary, Agata; Zawisławska, Magdalena

doi:10.1007/978-3-319-43808-5_17

Cited by 14 publications

(12 citation statements)

References 9 publications

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“…However, our system has only been tested on a single corpus so far, and its architecture is optimized for it. Further assessment of its capabilities should include further tests on other, comparable, corpora such as ARRAU (Poesio and Artstein 2008), the Polish Coreference Corpus (Ogrodniczuk et al 2016) or the upcoming DEMOCRAT corpus (Landragin 2016). Proper evaluation should also eventually include comparisons on the CoNLL-2012 dataset itself, possibly in the "gold mention boundaries" settings for a better comparability.…”

Section: Resultsmentioning

confidence: 99%

Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference

2019

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In many NLP applications like search and information extraction for named entities, it is necessary to find all the mentions of a named entity, some of which appear as pronouns (she, his, etc.) or nominals (the professor, the German chancellor, etc.). It is therefore important that coreference resolution systems are able to link these different types of mentions to the correct entity name. We evaluate state-of-theart coreference resolution systems for the task of resolving all mentions to named entities. Our analysis reveals that standard coreference metrics do not reflect adequately the requirements in this task: they do not penalize systems for not identifying any mentions by name to an entity and they reward systems even if systems find correctly mentions to the same entity but fail to link these to a proper name (she-the student-no name). We introduce new metrics for evaluating named entity coreference that address these discrepancies and show that for the comparisons of competitive systems, standard coreference evaluations could give misleading results for this task. We are, however, able to confirm that the state-of-the art system according to traditional evaluations also performs vastly better than other systems on the named entity coreference task.

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Section: Resultsmentioning

confidence: 99%

Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference

2019

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“…The CoNLL 2012 shared task added Arabic and Chinese. Less-resourced languages are also gaining presence in coreference resolution, as exemplified by recent work on Hungarian (Miháltz 2008), Polish(Ogrodniczuk and Kopeć 2011), Czech (Ngụy, Novák and Żabokrtský 2009), and also Indian Languages (Lalitha Devi, Sundar Ram and RK Rao 2014).…”

Section: Related Workmentioning

confidence: 99%

Improving mention detection for Basque based on a deep error analysis

Soraluze

Arregi

et al. 2016

Nat. Lang. Eng.

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This paper presents the improvement process of a mention detector for Basque. The system is rule-based and takes into account the characteristics of mentions in Basque. A classification of error types is proposed based on the errors that occur during mention detection. A deep error analysis distinguishing error types and causes is presented and improvements are proposed. At the final stage, the system obtains an F-measure of 74.57% under the Exact Matching protocol and of 80.57% under Lenient Matching. We also show the performance of the mention detector with gold standard data as input, in order to omit errors caused by the previous stages of linguistic processing. In this scenario, we obtain an F-measure of 85.89% with Strict Matching and of 89.06% with Lenient Matching, i.e., a difference of 11.32 and 8.49 percentage points, respectively. Finally, how improvements in mention detection affect coreference resolution is analysed.

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“…Mykowiecka et al [12]). It has been used to boost the accuracy of natural language processing on various tasks, and to develop many tools and resources for Polish such as the Concraft disambiguating tagger (Waszczuk [22]), Polish Dependency Corpus (Wróblewska [25]), Polish Coreference Corpus (Ogrodniczuk et al [13]), Hask collocation databases (Pęzik [15]), SEJF phraseological dictionary (Czerepowicka [1]) or Walenty valence dictionary (Hajnicz et al [6]). The National Corpus is cited as the basic resource of linguistic research in hundreds of publications.…”

Section: Lish Corpus Researchmentioning

confidence: 99%

From the National Corpus of Polish to the Polish Corpus Infrastructure

Ogrodniczuk

Górski

Łaziński

et al. 2019

Journal of Linguistics/Jazykovedný Casopis

Self Cite

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The National Corpus of Polish emerged as a cumulative result of many years of work on large reference corpora by computer scientists and linguists in Poland. While its impact on research in linguistics, humanities and language technology is unquestionable and highly significant, the construction of the national corpus was halted in 2011. In the paper we call for activating the research community and funding institutions around the construction of a corpus infrastructure with the national corpus at its heart. It is claimed that on the verge of an artificial intelligence revolution the envisaged Polish Corpus Infrastructure would provide reliable language data, combine available resources and allow easy integration of new ones.

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Polish Coreference Corpus

Cited by 14 publications

References 9 publications

Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference

Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference

Improving mention detection for Basque based on a deep error analysis

From the National Corpus of Polish to the Polish Corpus Infrastructure

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