5,630 research outputs found
Joint Extraction of Entities and Relations with a Hierarchical Multi-task Tagging Model
Entity extraction and relation extraction are two indispensable building
blocks for knowledge graph construction. Recent works on entity and relation
extraction have shown the superiority of solving the two problems in a joint
manner, where entities and relations are extracted simultaneously to form
relational triples in a knowledge graph. However, existing methods ignore the
hierarchical semantic interdependency between entity extraction (EE) and joint
extraction (JE), which leaves much to be desired in real applications. In this
work, we propose a hierarchical multi-task tagging model, called HMT, which
captures such interdependency and achieves better performance for joint
extraction of entities and relations. Specifically, the EE task is organized at
the bottom layer and JE task at the top layer in a hierarchical structure.
Furthermore, the learned semantic representation at the lower level can be
shared by the upper level via multi-task learning. Experimental results
demonstrate the effectiveness of the proposed model for joint extraction in
comparison with the state-of-the-art methods.Comment: 10 pages, 3 figure
A Hierarchical Framework for Relation Extraction with Reinforcement Learning
Most existing methods determine relation types only after all the entities
have been recognized, thus the interaction between relation types and entity
mentions is not fully modeled. This paper presents a novel paradigm to deal
with relation extraction by regarding the related entities as the arguments of
a relation. We apply a hierarchical reinforcement learning (HRL) framework in
this paradigm to enhance the interaction between entity mentions and relation
types. The whole extraction process is decomposed into a hierarchy of two-level
RL policies for relation detection and entity extraction respectively, so that
it is more feasible and natural to deal with overlapping relations. Our model
was evaluated on public datasets collected via distant supervision, and results
show that it gains better performance than existing methods and is more
powerful for extracting overlapping relations.Comment: To appear in AAAI 1
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