Integrating local context and global cohesiveness for open information extraction

open information extractionentity recognitionrelation extractionweakly-supervised learningdistant supervision

Zhu, Qi ; Han ; Jiawei

关键词: open information extractionentity recognitionrelation extractionweakly-supervised learningdistant supervision;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/101084/ZHU-THESIS-2018.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

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.