Integrating Local Context and Global Cohesiveness for Open Information Extraction

TL;DR

ReMine is a novel Open IE system that combines local sentence context and global corpus statistics within a unified framework, improving extraction quality across diverse domains.

Contribution

It introduces a joint optimization approach that integrates local phrase segmentation and global tuple quality assessment using distant supervision.

Findings

ReMine outperforms state-of-the-art systems on real-world datasets.

The system demonstrates high robustness and generality across domains.

Joint learning enhances extraction accuracy by correcting errors mutually.

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. These relation tuples are not confined to a predefined 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 unified, distant-supervision framework. Leveraging facts from external knowledge bases as supervision, the new system can be applied to many different 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 different domains demonstrate the effectiveness, generality, and robustness of ReMine when compared to state-of-the-art open IE systems.