CRF-Based Czech Named Entity Recognizer and Consolidation of Czech NER Research

Konkol, Michal; Konopík, Miloslav

doi:10.1007/978-3-642-40585-3_20

Cited by 47 publications

(34 citation statements)

References 9 publications

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“…For comparison with state of the art, Czech PDT UD 2.2 treebank without gold segmentation and tokenization is used in evaluation, according to the CoNLL 2018 shared task training and evaluation protocol. Our system reuses segmentation and tokenization produced by UDPipe 2.0 in the CoNLL [20] -79.00 -- Straková et al (2013) [33] 79.23 82.82 -- Straková et al (2016) [36] 81.20 84.68 79.23 82.78 Table 6. Named entity recognition results (F1) on the Czech Named Entity Corpus.…”

Section: Pos Tagging Lemmatization and Dependency Parsing On Universmentioning

confidence: 99%

Czech Text Processing with Contextual Embeddings: POS Tagging, Lemmatization, Parsing and NER

Straka

Straková

Hajič

2019

Text, Speech, and Dialogue

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Contextualized embeddings, which capture appropriate word meaning depending on context, have recently been proposed. We evaluate two methods for precomputing such embeddings, BERT and Flair, on four Czech text processing tasks: part-of-speech (POS) tagging, lemmatization, dependency parsing and named entity recognition (NER). The first three tasks, POS tagging, lemmatization and dependency parsing, are evaluated on two corpora: the Prague Dependency Treebank 3.5 and the Universal Dependencies 2.3. The named entity recognition (NER) is evaluated on the Czech Named Entity Corpus 1.1 and 2.0. We report state-of-the-art results for the above mentioned tasks and corpora.

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Section: Pos Tagging Lemmatization and Dependency Parsing On Universmentioning

confidence: 99%

Czech Text Processing with Contextual Embeddings: POS Tagging, Lemmatization, Parsing and NER

Straka

Straková

Hajič

2019

Text, Speech, and Dialogue

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“…carry sentiment and how their presence influences classification accuracy. For these experiments, we employ a CRF-based named entity recognizer (Konkol & Konopík, 2013) and replace the words identified as entities with their respective entity type (e.g., McDonald's becomes company). This preprocessing has not been widely discussed in the literature devoted to document-level sentiment analysis, but Boiy and Moens (2009), for example, remove the 'entity of interest' in their approach.…”

Section: Preprocessingmentioning

confidence: 99%

Reprint of “Supervised sentiment analysis in Czech social media”

Habernal

Ptáček

Shinar

2015

Information Processing & Management

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“…Prior work targeting NEs specifically for Slavic languages includes tools for NE recognition for Croatian (Karan et al, 2013;Ljubešić et al, 2013), a tool tailored for NE recognition in Croatian tweets (Baksa et al, 2017), a manually annotated NE corpus for Croatian (Agić and Ljubešić, 2014), tools for NE recognition in Slovene (Štajner et al, 2013;Ljubešić et al, 2013), a Czech corpus of 11,000 manually annotated NEs (Ševčíková et al, 2007), NER tools for Czech (Konkol and Konopík, 2013), tools and resources for fine-grained annotation of NEs in the National Corpus of Polish (Waszczuk et al, 2010;Savary and Piskorski, 2011) and a recent shared task on NE Recognition in Russian .…”

Section: Introductionmentioning

confidence: 99%

“…In 2010, the NEWS Workshop included a shared task on Transliteration Mining (Kumaran et al, 2010), i.e., mining of names from parallel corpora. This task included corpora in English, Chinese, Tamil, Russian, and Arabic.Prior work targeting NEs specifically for Slavic languages includes tools for NE recognition for Croatian (Karan et al, 2013;Ljubešić et al, 2013), a tool tailored for NE recognition in Croatian tweets (Baksa et al, 2017), a manually annotated NE corpus for Croatian (Agić and Ljubešić, 2014), tools for NE recognition in Slovene (Štajner et al, 2013;Ljubešić et al, 2013), a Czech corpus of 11,000 manually annotated NEs (Ševčíková et al, 2007), NER tools for Czech (Konkol and Konopík, 2013), tools and resources for fine-grained annotation of NEs in the National Corpus of Polish (Waszczuk et al, 2010;Savary and Piskorski, 2011) and a recent shared task on NE Recognition in Russian .To the best of our knowledge, the shared task described in this paper is the first attempt at multilingual name recognition, normalization, and cross-lingual entity matching that covers a large number of Slavic languages. This paper is organized as follows.…”

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confidence: 99%

Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing

2017

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The Workshops have been convening for over a decade, with a clear vision and purpose. On one hand, the languages from the Balto-Slavic group play an important role due to their widespread use and diverse cultural heritage. These languages are spoken by about one third of all speakers of the official languages of the European Union, and by over 400 million speakers worldwide. The political and economic developments in Central and Eastern Europe place societies where Balto-Slavic languages are spoken at the center of rapid technological advancement and the growing European consumer markets.On the other hand, research on theoretical and applied NLP in some of these languages still lags behind the "major" languages, such as English and other West European languages. In comparison to English, which has dominated the digital world since the advent of the Internet, many of these languages still lack resources, processing tools and applications-especially those with smaller speaker bases.The Balto-Slavic languages pose a wealth of fascinating scientific challenges. The linguistic phenomena specific to the Balto-Slavic languages-complex morphology and free word order-present non-trivial problems for construction of NLP tools, and require rich morphological and syntactic resources. This view is also reflected in Serge Sharoff's invited talk on "Pan-Slavic NLP." In the talk, he discusses an ambitious project on language adaptation-ways to adapt tools and resources among closely related languages, such as those in the Slavic group.The BSNLP Workshops aim to bring together academic researchers and industry specialists in NLP for Balto-Slavic languages. We aim to stimulate research and to foster the creation and dissemination of tools and resources. The Workshop serves as a forum for exchange of ideas and experience and for discussing shared problems. One fascinating aspect of this group of languages is their structural similarity, as well as an easily recognizable lexical and inflectional inventory spanning the entire group, which-despite the lack of mutual intelligibility-creates a special environment in which researchers can fully appreciate the shared problems and solutions.As a result of discussions at the previous BSNLP Workshops, to help catalyze collaboration, this year we have organized the first SIGSLAV Challenge: a shared task on multilingual named entity recognition. We have built a dataset, which allows systems to be evaluated on recognizing mentions of named entities in Web documents, their normalization/lemmatization, and cross-lingual matching. The Challenge initially covers seven Slavic languages, and it is intended as a first version of an evaluation standard to be expanded in the future.We received 24 regular submissions, 14 of which were accepted for presentation.The papers cover a wide range of topics. Two papers relate to lexical semantics, four to development of linguistic resources, and four to information filtering, information retrieval, and information extraction. Four papers cover topics related to...

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CRF-Based Czech Named Entity Recognizer and Consolidation of Czech NER Research

Cited by 47 publications

References 9 publications

Czech Text Processing with Contextual Embeddings: POS Tagging, Lemmatization, Parsing and NER

Czech Text Processing with Contextual Embeddings: POS Tagging, Lemmatization, Parsing and NER

Reprint of “Supervised sentiment analysis in Czech social media”

Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing

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