标题:Visualizing multi-document semantics via open domain information extraction
作者:Sheng, Yongpan ;Xu, Zenglin ;Wang, Yafang ;Zhang, Xiangyu ;Jia, Jia ;You, Zhonghui ;de Melo, Gerard
通讯作者:de Melo, Gerard
作者机构:[Sheng, Y] School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China;[ Xu, Z] School of Com 更多
会议名称:European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2018
会议日期:10 September 2018 through 14 September 2018
来源:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版年:2019
卷:11053 LNAI
页码:695-699
DOI:10.1007/978-3-030-10997-4_54
关键词:Graph-based visualization; Multi-document information extraction
摘要:Faced with the overwhelming amounts of data in the 24/7 stream of new articles appearing online, it is often helpful to consider only the key entities and concepts and their relationships. This is challenging, as relevant connections may be spread across a number of disparate articles and sources. In this paper, we present a system that extracts salient entities, concepts, and their relationships from a set of related documents, discovers connections within and across them, and presents the resulting information in a graph-based visualization. We rely on a series of natural language processing methods, including open-domain information extraction, a special filtering method to maintain only meaningful relationships, and a heuristic to form graphs with a high coverage rate of topic entities and concepts. Our graph visualization then allows users to explore these connections. In our experiments, we rely on a large collection of news crawled from the Web and show how connections within this data can be explored. Code related to this paper is available at: https://shengyp.github.io/vmse. © 2019, Springer Nature Switzerland AG.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061120292&doi=10.1007%2f978-3-030-10997-4_54&partnerID=40&md5=5dc7fc11554a3d50a2d6b2c27f0a0b44
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