@inproceedings{homma-etal-2016-hierarchical,
title = "A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences",
author = "Homma, Yukinori and
Sadamitsu, Kugatsu and
Nishida, Kyosuke and
Higashinaka, Ryuichiro and
Asano, Hisako and
Matsuo, Yoshihiro",
booktitle = "Proceedings of the Open Knowledge Base and Question Answering Workshop ({OKBQA} 2016)",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/W16-4403",
pages = "21--29",
abstract = "This paper describes a hierarchical neural network we propose for sentence classification to extract product information from product documents. The network classifies each sentence in a document into attribute and condition classes on the basis of word sequences and sentence sequences in the document. Experimental results showed the method using the proposed network significantly outperformed baseline methods by taking semantic representation of word and sentence sequential data into account. We also evaluated the network with two different product domains (insurance and tourism domains) and found that it was effective for both the domains.",
}
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%0 Conference Proceedings
%T A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences
%A Homma, Yukinori
%A Sadamitsu, Kugatsu
%A Nishida, Kyosuke
%A Higashinaka, Ryuichiro
%A Asano, Hisako
%A Matsuo, Yoshihiro
%S Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)
%D 2016
%8 dec
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F homma-etal-2016-hierarchical
%X This paper describes a hierarchical neural network we propose for sentence classification to extract product information from product documents. The network classifies each sentence in a document into attribute and condition classes on the basis of word sequences and sentence sequences in the document. Experimental results showed the method using the proposed network significantly outperformed baseline methods by taking semantic representation of word and sentence sequential data into account. We also evaluated the network with two different product domains (insurance and tourism domains) and found that it was effective for both the domains.
%U https://aclanthology.org/W16-4403
%P 21-29
Markdown (Informal)
[A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences](https://aclanthology.org/W16-4403) (Homma et al., 2016)
ACL