@inproceedings{hsiao-etal-2017-integrating,
title = "Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base",
author = "Hsiao, Wei-Chuan and
Huang, Hen-Hsen and
Chen, Hsin-Hsi",
editor = "Kondrak, Greg and
Watanabe, Taro",
booktitle = "Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/I17-1098",
pages = "976--985",
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.",
}
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%0 Conference Proceedings
%T Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base
%A Hsiao, Wei-Chuan
%A Huang, Hen-Hsen
%A Chen, Hsin-Hsi
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F hsiao-etal-2017-integrating
%X 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.
%U https://aclanthology.org/I17-1098
%P 976-985
Markdown (Informal)
[Integrating Subject, Type, and Property Identification for Simple Question Answering over Knowledge Base](https://aclanthology.org/I17-1098) (Hsiao et al., IJCNLP 2017)
ACL