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Investigation of features for extraction of named entities from texts in Russian

Investigation of features for extraction of named entities from texts in Russian This paper considers various features for extracting named entities from texts in Russian, which are used within the approaches based on machine learning, including the features of a token itself (lexeme), as well as vocabulary, contextual, cluster, and two-stage features. The contribution of each feature to improving the quality of extraction of named entities is studied. The CRF-classifier is used as a method of machine learning in the experiments that are described in this paper. The contribution of features is compared based on two open collections using the F-measure. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatic Documentation and Mathematical Linguistics Springer Journals

Investigation of features for extraction of named entities from texts in Russian

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Allerton Press, Inc.
Subject
Computer Science; Information Storage and Retrieval
ISSN
0005-1055
eISSN
1934-8371
DOI
10.3103/S0005105517030049
Publisher site
See Article on Publisher Site

Abstract

This paper considers various features for extracting named entities from texts in Russian, which are used within the approaches based on machine learning, including the features of a token itself (lexeme), as well as vocabulary, contextual, cluster, and two-stage features. The contribution of each feature to improving the quality of extraction of named entities is studied. The CRF-classifier is used as a method of machine learning in the experiments that are described in this paper. The contribution of features is compared based on two open collections using the F-measure.

Journal

Automatic Documentation and Mathematical LinguisticsSpringer Journals

Published: Aug 19, 2017

References