Incorporating Relation Paths in Neural Relation Extraction
This work addresses relation extraction for natural language processing by leveraging indirect textual evidence, offering an incremental improvement over existing methods.
The paper tackles the problem of distantly supervised relation extraction by incorporating sentences containing only one target entity through inference chains, achieving significant and consistent improvements over baselines on real-world datasets.
Distantly supervised relation extraction has been widely used to find novel relational facts from plain text. To predict the relation between a pair of two target entities, existing methods solely rely on those direct sentences containing both entities. In fact, there are also many sentences containing only one of the target entities, which provide rich and useful information for relation extraction. To address this issue, we build inference chains between two target entities via intermediate entities, and propose a path-based neural relation extraction model to encode the relational semantics from both direct sentences and inference chains. Experimental results on real-world datasets show that, our model can make full use of those sentences containing only one target entity, and achieves significant and consistent improvements on relation extraction as compared with baselines. The source code of this paper can be obtained from https: //github.com/thunlp/PathNRE.