KIISE Transactions on Computing Practices (정보과학회 컴퓨팅의 실제 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Coreference Resolution for Korean using Mention Pair with SVM

SVM 기반의 멘션 페어 모델을 이용한 한국어 상호참조해결

  • Received : 2014.09.15
  • Accepted : 2015.01.21
  • Published : 2015.04.15
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Abstract

In this paper, we suggest a Coreference Resolution system for Korean using Mention Pair with SVM. The system introduced in this paper, also be able to extract Mention from document which is including automatically tagged name entity information, dependency trees and POS tags. We also built a corpus, including 214 documents with Coreference tags, referencing online news and Wikipedia for training the system and testing the system's performance. The corpus had 14 documents from online news, along with 200 question-and-answer documents from Wikipedia. When we tested the system by corpus, the performance of the system was extracted by MUC-F1 55.68%, B-cube-F1 57.19%, and CEAFE-F1 61.75%.

본 논문에서는 품사태그가 부착된 의존구문 트리와 개체명 정보가 자동 태깅된 말뭉치에서 멘션(Mention)을 추출하고, SVM을 기반으로 한 멘션 페어 모델(Mention Pair Model) 이용하는 한국어 상호참조해결 시스템을 제안한다. 시스템의 학습과 평가를 위해서 신문기사를 기반으로 하는 14개의 문서와, 위키피디아(Wikipedia)를 기반으로 하는 200개의 질의응답 문서를 분석하여 상호참조해결 정보가 담긴 말뭉치를 구축했다. 실험결과 본 논문에서 제안한 시스템의 성능은 MUC-F1 55.68%, B-cube-F1 57.19%, CEAFE-F1 61.75% 로 나타났다.

Keywords

Acknowledgement

Grant : 휴먼 지식증강서비스를 위한 지능진화형 WiseQA 플랫폼 기술 개발

Supported by : 한국산업기술평가관리원

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Cited by

  1. Korean Coreference Resolution with Guided Mention Pair Model using the Deep Learning vol.38, pp.6, 2016, https://doi.org/10.4218/etrij.16.0115.0896