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oai:www.ideals.illinois.edu:2142/101084 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

2018-09-04T20:32:00Z 2018-04-26 2018-05 Han, Jiawei Zhu, Qi 2018-09-04T20:32:00Z 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. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms The student, Qi Zhu, accepted the attached license on 2018-04-25 at 16:18. The student, Qi Zhu, submitted this Thesis for approval on 2018-04-25 at 16:47. This Thesis was approved for publication on 2018-04-26 at 16:38. DSpace SAF Submission Ingestion Package generated from Vireo submission #12496 on 2018-08-31 at 17:14:58 Made available in DSpace on 2018-09-04T20:32:00Z (GMT). No. of bitstreams: 2 ZHU-THESIS-2018.pdf: 2223221 bytes, checksum: 87312b64be9803e3a9fcfab9ab4dad31 (MD5) LICENSE.txt: 4203 bytes, checksum: 7325468ea194464949f7fcc287108194 (MD5) Previous issue date: 2018-04-26 application/pdf http://hdl.handle.net/2142/101084 en Copyright 2018 Qi Zhu open information extraction entity recognition relation extraction weakly-supervised learning distant supervision Integrating local context and global cohesiveness for open information extraction text text Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.