Paper: Modeling Joint Entity and Relation Extraction with Table Representation

ACL ID D14-1200
Title Modeling Joint Entity and Relation Extraction with Table Representation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

This paper proposes a history-based struc- tured learning approach that jointly ex- tracts entities and relations in a sentence. We introduce a novel simple and flexible table representation of entities and rela- tions. We investigate several feature set- tings, search orders, and learning meth- ods with inexact search on the table. The experimental results demonstrate that a joint learning approach significantly out- performs a pipeline approach by incorpo- rating global features and by selecting ap- propriate learning methods and search or- ders.