Slavonic information extraction and partial parsing

Przepiórkowski, Adam

doi:10.3115/1567545.1567547

Cited by 12 publications

(8 citation statements)

References 30 publications

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“…Analyzing named entities (NEs) in Slavic languages poses a challenging problem, due to the rich inflection and derivation, free word order, and other morphological and syntactic phenomena exhibited in these languages (Przepiórkowski, 2007;Piskorski et al, 2009). Encouraging research on detection and normalization of NEs-and on the closely related problem of cross-lingual, crossdocument entity linking-is of paramount importance for improving multilingual and cross-lingual information access in these languages.…”

Section: Introductionmentioning

confidence: 99%

The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages

Piskorski¹,

Laskova²,

Marcińczuk³

et al. 2019

Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

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We describe the Second Multilingual Named Entity Challenge in Slavic languages. The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking. The Challenge was organized as part of the 7th Balto-Slavic Natural Language Processing Workshop, co-located with the ACL-2019 conference. Eight teams participated in the competition, which covered four languages and five entity types. Performance for the named entity recognition task reached 90% F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all four languages, and five teams participated in the cross-lingual entity linking task. Detailed evaluation information is available on the shared task web page.

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Section: Introductionmentioning

confidence: 99%

The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages

Piskorski¹,

Laskova²,

Marcińczuk³

et al. 2019

Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

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show abstract

“…For instance, in German, all nouns are capitalized; consequently the number of word forms to be considered as potential named entities is much larger [3]. In Slavonic languages the case of the noun phrase within a numerical phrase depends on the numeral and on the position of the whole numerical phrase in the sentence [4]. Likewise, NER for the languages with complex morphological structures, such as Turkish, requires a morphological level of processing.…”

Section: Introductionmentioning

confidence: 99%

Automatic rule learning exploiting morphological features for named entity recognition in Turkish

Tatar

Çiçekli

2011

Journal of Information Science

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Named entity recognition (NER) is one of the basic tasks in automatic extraction of information from natural language texts. In this paper, we describe an automatic rule learning method that exploits different features of the input text to identify the named entities located in the natural language texts. Moreover, we explore the use of morphological features for extracting named entities from Turkish texts. We believe that the developed system can also be used for other agglutinative languages. The paper also provides a comprehensive overview of the field by reviewing the NER research literature. We conducted our experiments on the TurkIE dataset, a corpus of articles collected from different Turkish newspapers. Our method achieved an average F-score of 91.08% on the dataset. The results of the comparative experiments demonstrate that the developed technique is successfully applicable to the task of automatic NER and exploiting morphological features can significantly improve the NER from Turkish, an agglutinative language.

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“…The length of the list arises from some of the features of Polish, typical for Slavonic languages, i.e. relatively free word order and rich nominal inflection (Przepiórkowski, 2007). For example one English pattern Whose .…”

Section: Question Type Classificationmentioning

confidence: 99%

“…Both the focus removal and synonym expansion have been implemented as options of the presented query formation mechanism. Finally, one needs to remember about an important feature of Polish, typical for a Slavonic language, namely rich nominal inflection (Przepiórkowski, 2007). It means that the orthographic forms of nouns change as they appear in different roles in a sentence.…”

Section: Query Formationmentioning

confidence: 99%

Question Classification for Polish Question Answering

Przybyła

2013

Language Processing and Intelligent Information Systems

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This study is devoted to the problem of question analysis for a Polish question answering system. The goal of the question analysis is to determine its general structure, type of an expected answer and create a search query for finding relevant documents in a textual knowledge base. The paper contains an overview of available solutions of these problems, description of their implementation and presents an evaluation based on a set of 1137 questions from a Polish quiz TV show. The results help to understand how an environment of a Slavonic language affects the performance of methods created for English.

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Slavonic information extraction and partial parsing

Cited by 12 publications

References 30 publications

The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages

The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages

Automatic rule learning exploiting morphological features for named entity recognition in Turkish

Question Classification for Polish Question Answering

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