IDENTIFYING MORPHOLOGICAL PROPERTIES OF RUSSIAN WORDS WITH THE ONTOLOGY-BASED TEXT ANALYSER

Year 2017, Volume: 3 Issue: 8, 443 - 449, 31.08.2017

Ksenia Balysheva Elena Kartashova Konstantin Kondratiev Aleksey Mikheev

https://doi.org/10.18769/ijasos.336974

Abstract

This article presents the first
stage of an ongoing effort of creating the application Ontology-Based Text
Analyser (OTA) aimed at automatically identifying semantics and grammatical
properties of widely used Russian words in connected texts. At present this
application identifies only morphological properties of Russian words. In this
application all morphological properties of content words and grammatical
function words are revealed on the basis of a query to the Ontology of Russian
Grammatical Forms (OntoRuGrammaForm) that we set up earlier. In
OntoRuGrammaForm we used LexInfo which represents morphological
properties of words in the ontological format as a scheme for data organising.
To set up OntoRuGrammaForm the existing LexInfo ontology was extended with
missing and refined grammatical categories. In OntoRuGrammaForm the linkage of
semantics with morphological properties is implemented with OntoLex which makes it possible to link
grammatical word forms with lemmas and lemmas with concepts in knowledge area
ontologies. The automatic process of word morphology identification is
illustrated with a connected text of the informative type taken from the open
news online-portal. In this news text the system of morphological properties of
words is identified with OntoRuGrammaForm. This application also displays
lemmas and transcription of separate words in a connected text. The created
application (OTA) can be used as an innovative methodical tool in teaching
Russian to foreign students to develop skills of identifying morphological characteristics
of words in texts. At present this application is available on the Web in open
access and can be used for analysing morphological properties of widely used
Russian words in a connected text.

Keywords

Automatic identifying, Morphological properties, Ontology, Ontology-based application, Text analyser, LexInfo, OntoLex

References

• Chiarcos, C. (2008). An ontology of linguistic annotations. LDV Forum, pp. 1–136. Chiarcos, Ch., McCrae, J., Cimiano, Ph., Fellbaum, Ch. (2013). Towards open data for linguistics: linguistic linked data, new trends of research in ontologies and lexical resources. Springer. Cimiano, P., McCrae, J., Buitelaar, P., Stintek, M. (2011). Lexinfo: A declarative model for the lexicon-ontology interface. Web Semantics: Science, Services and Agents on the World Wide Web, pp. 29–51. Cimiano, Ph., Unger, Ch., McCrae, J. (2014). Ontology-based interpretation of natural language. Gruber, T. (1993). Toward principles for the design of ontologies used for knowledge sharing. Formal Analysis in Conceptual Analysis and Knowledge Representation. Kluwer. Guarino, N. (1998). Some ontological principles for designing upper level lexical resources. Proceedings of First International Conference on Language Resources and Evaluation (LREC), pp. 527–534. Guarino, N., Welty, Ch. (2004). An overview of OntoClean, handbook on ontologies. Springer., pp. 151–159, Springer. Kruglikova, L.E. (2012). The Great academic dictionary of the Russian language as a successor of Russian academic lexicography traditions. Notebooks of Spanish Studies of Russian Philology, vol. 8, pp. 177 – 198. Loukachevitch, N.V. (2010). Thesauri in tasks of information retrieval. Moscow. McCrae, J., Spohr, D., and Cimiano, P. (2011). Linking lexical resources and ontologies on the semantic web with lemon. The Semantic Web: Research and Applications, pp. 245–259 Miller, G. (1998). Nouns in WordNet, Fellbaum, C (ed) WordNet, An Electronic Lexical Database, The MIT Press, pp.23-47. Navigli, R., Ponzetto, S. (2010) BabelNet: building a very large multilingual semantic network. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 216 –225. Ozhegov, S.I. (1983). Dictionary of the Russian language (in Russian). Moscow. Studer, R., Benjamins, V., and Fensel, D. (1998). Knowledge engineering: Principles and methods. Data Knowledge Engineering, pp. 161–197.