Joint entity recognition and relation extraction as a multi-head selection problem

• DOI: 10.1016/j.eswa.2018.07.032
• Publication Date: 2018-07-17T15:26:22Z
• Authors: Giannis Bekoulis; Johannes Deleu; Thomas Demeester; Chris Develder
• Keywords: Multi-head selection; FOS: Computer and information sciences; Entity recognition; model; Computer Science - Computation and Language; 02 engineering and technology; NETWORKS; Joint; 0202 electrical engineering, electronic engineering, information engineering; Relation extraction; Sequence labeling; Computation and Language (cs.CL)

Expert Systems with Applications, code is available at https://github.com/bekou/multihead_joint_entity_relation_extraction<br/>State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers. Thus, the performance of such joint models depends on the quality of the features obtained from these NLP tools. However, these features are not always accurate for various languages and contexts. In this paper, we propose a joint neural model which performs entity recognition and relation extraction simultaneously, without the need of any manually extracted features or the use of any external tool. Specifically, we model the entity recognition task using a CRF (Conditional Random Fields) layer and the relation extraction task as a multi-head selection problem (i.e., potentially identify multiple relations for each entity). We present an extensive experimental setup, to demonstrate the effectiveness of our method using datasets from various contexts (i.e., news, biomedical, real estate) and languages (i.e., English, Dutch). Our model outperforms the previous neural models that use automatically extracted features, while it performs within a reasonable margin of feature-based neural models, or even beats them.<br/>