Rich Set of Features for Proper Name Recognition in Polish Texts

Micha,; Marci,; czuk,; Stanek,; Piasecki, Maciej; Musia, Adam

doi:10.1007/978-3-642-25261-7_26

Cited by 10 publications

(3 citation statements)

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“…The conditions are: -(word) starts with an upper case letter, -starts with a lower case letter, -starts with a symbol, -starts with a digit, -contains upper case letter, -contains a lower case letter, -contains a symbol -contains digit. The features are based on filtering rules described in (Marcińczuk and Piasecki, 2011), e.g., first names and surnames start from upper case and do not contain symbols. To some extent these features duplicate the pattern feature.…”

Section: Orthographic Featuresmentioning

confidence: 99%

“…It provides a set of modules (based on statistical models, dictionaries, rules and heuristics) which recognize and annotate certain types of phrases. The framework was already used for recognition of named entities (different levels of granularity, including boundaries, coarse-and fine-grained categories) (Marcińczuk et al, 2012), temporal expressions (Kocoń and Marcińczuk, 2016b) and event mentions (Kocoń and Marcińczuk, 2016a) for Polish. Figure 1: Precision (P), recall (R) and F-measure (F) for various task obtained with Liner2.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Orthographic Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2017

View full text Add to dashboard Cite

show abstract

“…It provides a set of modules (based on statistical models, dictionaries, rules and heuristics) which recognize and annotate certain types of phrases. The framework was already used for recognition of named entities (different levels of granularity, including boundaries, coarse-and fine-grained categories) (Marcińczuk et al, 2012), temporal expressions (Kocoń and Marcińczuk, 2016b) and event mentions (Kocoń and Marcińczuk, 2016a) for Polish. Table 1 contains results for various tasks obtained using Liner2.…”

Section: Introductionmentioning

confidence: 99%