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Extracting entities and their relations from text is an important task forunderstanding massive text corpora. Open information extraction (IE) systemsmine relation tuples (i.e., entity arguments and a predicate string to describetheir relation) from sentences. These relation tuples are not confined to apredefined schema for the relations of interests. However, current Open IEsystems focus on modeling local context information in a sentence to extractrelation tuples, while ignoring the fact that global statistics in a largecorpus can be collectively leveraged to identify high-quality sentence-levelextractions. In this paper, we propose a novel Open IE system, called ReMine,which integrates local context signals and global structural signals in aunified, distant-supervision framework. Leveraging facts from externalknowledge bases as supervision, the new system can be applied to many differentdomains to facilitate sentence-level tuple extractions using corpus-levelstatistics. Our system operates by solving a joint optimization problem tounify (1) segmenting entity/relation phrases in individual sentences based onlocal context; and (2) measuring the quality of tuples extracted fromindividual sentences with a translating-based objective. Learning the twosubtasks jointly helps correct errors produced in each subtask so that they canmutually enhance each other. Experiments on two real-world corpora fromdifferent domains demonstrate the effectiveness, generality, and robustness ofReMine when compared to state-of-the-art open IE systems.

Integrating Local Context and Global Cohesiveness for Open Information Extraction