Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base

Wei-Chuan Hsiao, Hen-Hsen Huang, Hsin-Hsi Chen


Abstract
This paper presents an approach to identify subject, type and property from knowledge base (KB) for answering simple questions. We propose new features to rank entity candidates in KB. Besides, we split a relation in KB into type and property. Each of them is modeled by a bi-directional LSTM. Experimental results show that our model achieves the state-of-the-art performance on the SimpleQuestions dataset. The hard questions in the experiments are also analyzed in detail.
Anthology ID:
I17-1098
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Editors:
Greg Kondrak, Taro Watanabe
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
976–985
Language:
URL:
https://aclanthology.org/I17-1098
DOI:
Bibkey:
Cite (ACL):
Wei-Chuan Hsiao, Hen-Hsen Huang, and Hsin-Hsi Chen. 2017. Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 976–985, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base (Hsiao et al., IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-1098.pdf
Data
SimpleQuestions