Papers by Bartłomiej Nitoń
Introducing the CURLICAT Corpora: Seven-language Domain Specific Annotated Corpora from Curated Sources (2022.lrec-1)
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Tamás Váradi, Bence Nyéki, Svetla Koeva, Marko Tadić, Vanja Štefanec, Maciej Ogrodniczuk, Bartłomiej Nitoń, Piotr Pęzik, Verginica Barbu Mititelu, Elena Irimia, Maria Mitrofan, Dan Tufiș, Radovan Garabík, Simon Krek, Andraž Repar
| Challenge: | The CURLICAT CEF Telecom project aims to collect and deeply annotate a set of large corpora from selected domains. |
| Approach: | They present the results of the CURLICAT CEF Telecom project . they propose to collect and deeply annotate a set of large corpora from selected domains . |
| Outcome: | The CURLICAT CEF Telecom project provides a set of large corpora from selected domains . the corporatized corporates are tokenized, lemmatized and morphologically analysed . |
HerBERT Based Language Model Detects Quantifiers and Their Semantic Properties in Polish (2022.lrec-1)
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| Challenge: | a tool for automatic marking up of quantifiers is proposed for Polish . it is trained on a recently annotated corpus of Polish quantificational expressions . |
| Approach: | They propose to use a BERT based neural model to mark up quantifiers in text . they analyse a manually annotated corpus of Polish quantificational expressions and compare it to a human annotation model. |
| Outcome: | The proposed model can be used to build semantically annotated quantifier corpora for other languages. |
Deep Neural Networks for Coreference Resolution for Polish (L18-1)
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| Challenge: | Existing deep neural networks for coreference resolution for Polish have been used to resolve textual fragments that refer to the same entity in the discourse world. |
| Approach: | They propose a system combining the best deep neural architecture and sieve-based coreference resolvers ordered from most to least precise to achieve the highest results. |
| Outcome: | The proposed system improves the state of the art for Polish by 0.53 F1 points, reaching 81.23 points of the CoNLL metric. |
The MARCELL Legislative Corpus (2020.lrec-1)
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Tamás Váradi, Svetla Koeva, Martin Yamalov, Marko Tadić, Bálint Sass, Bartłomiej Nitoń, Maciej Ogrodniczuk, Piotr Pęzik, Verginica Barbu Mititelu, Radu Ion, Elena Irimia, Maria Mitrofan, Vasile Păiș, Dan Tufiș, Radovan Garabík, Simon Krek, Andraz Repar, Matjaž Rihtar, Janez Brank
| Challenge: | MARCELL corpus provides a rich and valuable source for further studies and developments in machine learning, cross-lingual terminological data extraction and classification. |
| Approach: | They present the results of the project MARCELL CEF Telecom . they aim to collect and deeply annotate a large comparable corpus of legal documents . |
| Outcome: | The MARCELL corpus includes 7 monolingual sub-corpora containing the body of respective national legislative documents. |