Integrating local context and global cohesiveness for open information extraction
Zhu, Qi
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https://hdl.handle.net/2142/101084
Description
Title
Integrating local context and global cohesiveness for open information extraction
Author(s)
Zhu, Qi
Issue Date
2018-04-26
Director of Research (if dissertation) or Advisor (if thesis)
Han, Jiawei
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(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.
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