Modeling Joint Entity and Relation Extraction with Table Representation
Makoto Miwa, Yutaka Sasaki · 2014
This paper proposes a history-based structured learning approach that jointly extracts entities and relations in a sentence.We introduce a novel simple and flexible table representation of entities and relations.We investigate several feature settings, search orders, and learning methods with inexact search on the table.The experimental results demonstrate that a joint learning approach significantly outperforms a pipeline approach by incorporating global features and by selecting appropriate learning methods and search orders.