Mining Entity-Relation-Attribute Structures from Massive Text Data

author: Xiang Ren, University of Illinois at Urbana-Champaign
author: Jiawei Han, Department of Computer Science, University of Illinois at Urbana-Champaign
published: Nov. 21, 2017,   recorded: August 2017,   views: 1107

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Entity-Relation-Attribute (ERA) structures, forming structured networks between entities and attributes, have demonstrated the flexibility of storing rich information and the effectiveness of gaining insights and knowledge. However, the majority of massive amount of data in the real world are unstructured text, ranging from news articles, social media post, advertisements, to a wide range of textual information from various domains (medical records, corporate reports). Without heavy human annotations and curations, most of existing approaches have difficulties in extracting named entities and their relations as well as typing and organizing knowledge as networks.

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