Files in this item



application/pdfZHU-THESIS-2018.pdf (2MB)
(no description provided)PDF


Title:Integrating local context and global cohesiveness for open information extraction
Author(s):Zhu, Qi
Advisor(s):Han, Jiawei
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):open information extraction entity recognition relation extraction weakly-supervised learning distant supervision
Abstract:Extracting entities and their relations from text is an important task for understanding massive text corpora. Open information extraction (IE) systems mine relation tuples (i.e., entity arguments and a predicate string to describe their relation) from sentences, and do not confine to a pre-defined schema for the relations of interests. However, current open IE systems focus on modeling local context information in a sentence to extract relation tuples, while ignoring the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions. In this paper, we propose a novel open IE system, called ReMine, which integrates local context signal and global structural signal in a unified framework with distant supervision. The new system can be efficiently applied to different domains as it uses facts from external knowledge bases as supervision; and can effectively score sentence-level tuple extractions based on corpus-level statistics. Specifically, we design a joint optimization problem to unify (1) segmenting entity/relation phrases in individual sentences based on local context; and (2) measuring the quality of sentence-level extractions with a translating-based objective. Experiments on two real-world corpora from different domains demonstrate the effectiveness and robustness of ReMine when compared to other open IE systems.
Issue Date:2018-04-26
Rights Information:Copyright 2018 Qi Zhu
Date Available in IDEALS:2018-09-04
Date Deposited:2018-05

This item appears in the following Collection(s)

Item Statistics