首页> 外文会议>Annual meeting of the Association for Computational Linguistics;Meeting of the Association for Computational Linguistics >A Semi-Supervised Key Phrase Extraction Approach: Learning from Title Phrases through a Document Semantic Network
【24h】

A Semi-Supervised Key Phrase Extraction Approach: Learning from Title Phrases through a Document Semantic Network

机译:半监督的关键短语提取方法:通过文档语义网络从标题短语中学习

获取原文

摘要

It is a fundamental and important task to extract key phrases from documents. Generally, phrases in a document are not independent in delivering the content of the document. In order to capture and make better use of their relationships in key phrase extraction, we suggest exploring the Wikipedia knowledge to model a document as a semantic network, where both n-ary and binary relationships among phrases are formulated. Based on a commonly accepted assumption that the title of a document is always elaborated to reflect the content of a document and consequently key phrases tend to have close semantics to the title, we propose a novel semi-supervised key phrase extraction approach in this paper by computing the phrase importance in the semantic network, through which the influence of title phrases is propagated to the other phrases iteratively. Experimental results demonstrate the remarkable performance of this approach.
机译:从文档中提取关键短语是一项基本而重要的任务。通常,文档中的短语在传递文档内容方面并不独立。为了在关键短语提取中捕获并更好地利用它们之间的关系,我们建议您探索Wikipedia知识,以将文档建模为语义网络,在该网络中,表达短语之间的n元和二进制关系。基于一个普遍接受的假设,即文档标题总是经过精心设计以反映文档的内容,因此,关键短语倾向于与标题具有紧密的语义,因此,我们在本文中提出了一种新颖的半监督关键字短语提取方法计算语义网络中短语的重要性,通过该重要性网络将标题短语的影响迭代地传播到其他短语。实验结果证明了这种方法的卓越性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号