Multi-labeled Relation Extraction with Attentive Capsule Network
This addresses the challenge of multi-labeled relation extraction in natural language processing, which is incremental as it builds on existing neural models but introduces specific enhancements for overlapping features.
The paper tackles the problem of extracting multiple overlapping relations from a single sentence by proposing a novel approach using a capsule network with attention-based routing and a sliding-margin loss, achieving significant performance improvement compared to state-of-the-art methods.
To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works.