A hybrid approach for anaphora resolution in the Russian language

Anna Kozlova, Alexey Svischev, Olga Gureenkova, Tatiana Batura

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

4 Citations (Scopus)

Abstract

The paper is dedicated to applying a hybrid approach based on rules and machine learning for anaphora resolution in the Russian language. The model combines formal rules, the Extra Trees machine learning algorithm and the Balance Cascade algorithm for working with imbalanced learning sets. A number of features were obtained from the rules or were generated from other features; in addition, the syntactic context was taken into account. A neural network algorithm SyntaxNet was used to analyze the syntactic context.

Original languageEnglish
Title of host publicationProceedings - 2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages36-40
Number of pages5
ISBN (Electronic)9781538615935
DOIs
Publication statusPublished - 18 Oct 2017
Event2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017 - Novosibirsk, Akademgorodok, Russian Federation
Duration: 12 Apr 201713 Apr 2017

Conference

Conference2017 Siberian Symposium on Data Science and Engineering, SSDSE 2017
CountryRussian Federation
CityNovosibirsk, Akademgorodok
Period12.04.201713.04.2017

Keywords

  • anaphora
  • antecedent
  • classification
  • coreference
  • ensemble learning
  • machine learning

OECD FOS+WOS

  • 1.02.ET COMPUTER SCIENCE, INFORMATION SYSTEMS
  • 1.02.EW COMPUTER SCIENCE, SOFTWARE ENGINEERING
  • 2.02.AC AUTOMATION & CONTROL SYSTEMS
  • 2.02 ELECTRICAL ENG, ELECTRONIC ENG

State classification of scientific and technological information

  • 28.23 Artificial intelligence

Fingerprint

Dive into the research topics of 'A hybrid approach for anaphora resolution in the Russian language'. Together they form a unique fingerprint.

Cite this