Mention Detection for Improving Coreference Resolution in Russian Texts: A Machine Learning Approach

Authors

  • Svetlana Toldova National Research University "Higher School of Economics"
  • Max Ionov Moscow State University / Goethe University Frankfurt

DOI:

https://doi.org/10.13053/cys-20-4-2480

Keywords:

Coreference resolution, discourse-new detection, singleton detection, discourse processing, natural language processing, machine learning.

Abstract

The paper concerns discourse-new referent detection. The task of coreference resolution is essential in many text-mining applications. The focus in this task is to detect noun phrases (NPs) that refer to the same entity. In languages without articles, there are no overt grammatical clues in an NP for whether it introduces a new referent into discourse or it refers to one of before-mentioned entities. However, there are some theoretical researches which claim that referent first-mentioning NPs have some specific features. In our research, we examine features that serve as discourse-new detectors for NPs corresponding to discourse salient referents and provide an experiment on different features contribution to this detection. The first-mention detection could help the quality of coreference resolution systems.

Author Biographies

Svetlana Toldova, National Research University "Higher School of Economics"

Holds PhD in computational linguistics from Moscow State University. She is currently research professor at National Research Institute “Higher School of Economics”, Moscow, Russia. Her research areas are natural language processing (anaphora resolution, parsing, etc.)

Max Ionov, Moscow State University / Goethe University Frankfurt

Ionov is Graduate Student at Moscow State University, Department of Theoretical and Applied Linguistics, Moscow, Russia. His research areas are natural language processing (anaphora resolution, parsing, etc.).

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Published

2016-12-18

Issue

Vol. 20 No. 4 (2016): Thematic Issue: Research Advances and Applications of Evolutionary Computation (Guest Editors: H.J. Escalante, M. Graff)

Section

Articles

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