Hierarchical Type Constrained Topic Entity Detection for Knowledge Base Question Answering

15Citations
Citations of this article
26Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Topic entity detection is to find out the main entity asked in a question, which is significant in question answering. Traditional methods ignore the information of entities, especially entity types and their hierarchical structures, restricting the performance. To take full advantage of Knowledge Base(KB) and detect topic entities correctly, we propose a deep neural model to leverage type hierarchy and relations of entities in KB. Experimental results demonstrate the effectiveness of the proposed method.

Cite

CITATION STYLE

APA

Qiu, Y., Li, M., Wang, Y., Jia, Y., & Jin, X. (2018). Hierarchical Type Constrained Topic Entity Detection for Knowledge Base Question Answering. In The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018 (pp. 35–36). Association for Computing Machinery, Inc. https://doi.org/10.1145/3184558.3186916

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free