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计算机工程 ›› 2020, Vol. 46 ›› Issue (10): 74-80,87. doi: 10.19678/j.issn.1000-3428.0055582

• 人工智能与模式识别 • 上一篇    下一篇

基于汉语框架语义的共指消解研究

吕国英1a, 武宇娟1a, 李茹1a,1b,2, 张月平1a, 关勇1a, 郭少茹1a   

  1. 1. 山西大学 a. 计算机与信息技术学院;b. 计算智能与中文信息处理教育部重点实验室, 太原 030006;
    2. 山西省大数据挖掘与智能技术协同创新中心, 太原 030006
  • 收稿日期:2019-07-25 修回日期:2019-10-16 发布日期:2019-10-31
  • 作者简介:吕国英(1964-),女,教授,主研方向为自然语言处理;武宇娟,硕士研究生;李茹,教授、博士、博士生导师;张月平,硕士研究生;关勇、郭少茹,博士研究生。
  • 基金资助:
    国家社会科学基金(18BYY009)。

Research on Coreference Resolution Based on Chinese Frame Semantics

Lü Guoying1a, WU Yujuan1a, LI Ru1a,1b,2, ZHANG Yueping1a, GUAN Yong1a, GUO Shaoru1a   

  1. 1a. School of Computer and Information Technology;1b. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, China;
    2. Collaborative Innovation Center of Big Data Mining and Intelligent Technology in Shanxi Province, Taiyuan 030006, China
  • Received:2019-07-25 Revised:2019-10-16 Published:2019-10-31

摘要: 基于框架语义的推理是实现语篇理解、问答系统等任务中语义理解的一种有效手段,框架语义推理通过构建汉语篇章句子框架之间的联系寻找推理路径,但框架元素内部的表述共指阻碍了框架之间联系的建立。针对该问题,提出一种基于框架特征的共指消解方法,该方法通过融合汉语框架语义信息并采用多种分类算法实现共指消解。框架语义篇章语料集上的实验结果表明,将汉语框架特征应用于分类器上能够较好地提升共指消解结果,且支持向量机的分类效果优于朴素贝叶斯、决策树等分类算法。

关键词: 汉语框架网, 框架语义, 共指消解, 框架元素, 共指链

Abstract: Reasoning based on frame semantics is an effective means to achieve semantic understanding in tasks such as discourse comprehension and QA system.It finds the inferential path by constructing connections between the frames of sentences in Chinese texts,but the coreference of the internal representations of the frame elements hinders the establishment of connections between the frames.To address the problem,this paper proposes a coreference resolution method based on frame features,which integrates the Chinese frame semantic information,and uses different classification algorithms to achieve coreference resolution.Experimental results on the corpus of frame semantics discourses show that the application of Chinese frame features to classifiers can improve the results of coreference resolution,and the classification performance of support vector machine is better than that of naive Bayesian,decision tree and other classifiers.

Key words: Chinese FrameNet(CFN), frame semantics, coreference resolution, frame element, coreference chain

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