Integrating Local Context and Global Cohesiveness for Open Information Extraction

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. ese relation tuples are not con ned to a prede ned 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 signals and global structural signals in a uni ed, distant-supervision framework. Leveraging facts from external knowledge bases as supervision, the new system can be applied to many di erent domains to facilitate sentence-level tuple extractions using corpus-level statistics. Our system operates by solving a joint optimization problem to unify (1) segmenting entity/relation phrases in individual sentences based on local context; and (2) measuring the quality of tuples extracted from individual sentences with a translating-based objective. Learning the two subtasks jointly helps correct errors produced in each subtask so that they can mutually enhance each other. Experiments on two real-world corpora from di erent domains demonstrate the e ectiveness, generality, and robustness of ReMine when compared to state-of-the-art open IE systems.

Arabic open information extraction system using dependency parsing

“…These categories are determined based on the number of clauses in the sentence. Table 4 shows the results containing each category's ratio and precision in every field [10], [35].…”

Interpreting Open-Domain Modifiers: Decomposition of Wikipedia Categories into Disambiguated Property-Value Pairs

“…As it extracts semantic annotations over opendomain concepts (namely, over categories from Wikipedia), the proposed method falls under the area of open-domain information extraction (Ernst et al, 2018;Qu et al, 2018;Sun et al, 2018;Zhu et al, 2019;Zhan and Zhao, 2020;Dash et al, 2020;Cao et al, 2020). Previous work in that area often uses Wikipedia data (Tsurel et al, 2017;Konovalov et al, 2017;Korn et al, 2019;Bornemann et al, 2020).…”

A Review on Document Information Extraction Approaches

“…Such examples would be (Corro and Gemulla, 2013) and (Yates et al, 2007) discussing legacy open information extraction approaches. Later, the works of (Zhu et al, 2019), (Jia et al, 2019, (Zhang et al, 2019a) and (Zhang et al, 2019b) explains more advanced approaches in open information extraction, but this is not the scope in this paper as this emphasis on document information extraction aspects.…”