@inproceedings{azerkovich-2019-using,
title = "Using Thesaurus Data to Improve Coreference Resolution for {R}ussian",
author = "Azerkovich, Ilya",
editor = "Vossen, Piek and
Fellbaum, Christiane",
booktitle = "Proceedings of the 10th Global Wordnet Conference",
month = jul,
year = "2019",
address = "Wroclaw, Poland",
publisher = "Global Wordnet Association",
url = "https://aclanthology.org/2019.gwc-1.6",
pages = "39--45",
abstract = "Semantic information about entities, specifically, how close in meaning two mentions are to each other, can become very useful for the task of co-reference resolution. One of the most well-researched and widely used forms of presenting this information are measures of semantic similarity and semantic relatedness. These metrics are often computed, relying upon the structure of a thesaurus, but it is also possible to use alternative resources. One such source is Wikipedia, which possesses the category structure similar to that of a thesaurus. In this work we describe an attempt to use semantic relatedness measures, calculated on thesaurus and Wikipedia data, to improve the quality of a co-reference resolution system for Russian language. The results show that this is a viable solution and that combining the two sources yields the most gain in quality.",
}
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%0 Conference Proceedings
%T Using Thesaurus Data to Improve Coreference Resolution for Russian
%A Azerkovich, Ilya
%Y Vossen, Piek
%Y Fellbaum, Christiane
%S Proceedings of the 10th Global Wordnet Conference
%D 2019
%8 July
%I Global Wordnet Association
%C Wroclaw, Poland
%F azerkovich-2019-using
%X Semantic information about entities, specifically, how close in meaning two mentions are to each other, can become very useful for the task of co-reference resolution. One of the most well-researched and widely used forms of presenting this information are measures of semantic similarity and semantic relatedness. These metrics are often computed, relying upon the structure of a thesaurus, but it is also possible to use alternative resources. One such source is Wikipedia, which possesses the category structure similar to that of a thesaurus. In this work we describe an attempt to use semantic relatedness measures, calculated on thesaurus and Wikipedia data, to improve the quality of a co-reference resolution system for Russian language. The results show that this is a viable solution and that combining the two sources yields the most gain in quality.
%U https://aclanthology.org/2019.gwc-1.6
%P 39-45
Markdown (Informal)
[Using Thesaurus Data to Improve Coreference Resolution for Russian](https://aclanthology.org/2019.gwc-1.6) (Azerkovich, GWC 2019)
ACL