Ontological Parsing of Encyclopedia Information

Bocharov, Victor; Pivovarova, Lidia; Rubashkin, Valery; Чуприн, Б. Ю.

doi:10.1007/978-3-642-12116-6_48

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…As far as we aware, this is the first work on the application of pattern learning techniques for the Russian language, despite general interest in Information Extraction (Starostin et al, 2016) and building of linguistic resources (Loukachevitch and Dobrov, 2014;. Bocharov et al (2010) and Sabirova and Lukanin (2014) used rule-based approach to extract taxonomic relations from text. Kuznetsov et al (2016) applied a number of machine learning techniques to automatic relation extraction from the Russian Wikipedia but their method depends on the specific structure of Wikipedia.…”

Section: Number Of Instances Precisionmentioning

confidence: 99%

Towards Never Ending Language Learning for Morphologically Rich Languages

Buraya

Pivovarova²,

Budkov

et al. 2017

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

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This work deals with ontology learning from unstructured Russian text. We implement one of the components of Never Ending Language Learner and introduce the algorithm extensions aimed to gather specificity of morphologically rich freeword-order language. We perform several experiments comparing different settings of the training process. We demonstrate that morphological features significantly improve the system precision while seed patterns help to improve the coverage.

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Section: Number Of Instances Precisionmentioning

confidence: 99%

Towards Never Ending Language Learning for Morphologically Rich Languages

Buraya

Pivovarova²,

Budkov

et al. 2017

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Bocharov et al (2010) and Sabirova and Lukanin (2014) used rule-based approach to extract taxonomic relations from text. Kuznetsov et al (2016) applied a number of machine learning techniques to automatic relation extraction from the Russian Wikipedia but their method depends on the specific structure of Wikipedia.…”

Section: Related Workmentioning

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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The development of a system for automated ontology building based on heterogeneous ontology design patterns

Zagorulko

Borovikova

Zagorulko

2021

J. Phys.: Conf. Ser.

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Currently, ontologies have become the main means of formalizing and systematizing knowledge and data in scientific subject domains (SSDs). In this regard, there is an urgent need for the methods and software tools allowing to involve specialists from different scientific subject domains in the process of ontology building. A possible way to solve this problem is to use ontology design patterns (ODPs). The paper presents an approach to the development of a system supporting the automated construction of the SSD ontologies based on ontology design patterns, which are the formal descriptions of field-proven solutions to typical ontological modeling problems. The main components of this system are repositories of heterogeneous ODPs and basic ontologies, including concepts common to most SSDs, and editors of patterns and ontologies used to build and populate ontologies.

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Ontological Parsing of Encyclopedia Information

Cited by 4 publications

References 6 publications

Towards Never Ending Language Learning for Morphologically Rich Languages

Towards Never Ending Language Learning for Morphologically Rich Languages

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

The development of a system for automated ontology building based on heterogeneous ontology design patterns

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