@InProceedings{homma-EtAl:2016:OKBQA2016,
  author    = {Homma, Yukinori  and  Sadamitsu, Kugatsu  and  Nishida, Kyosuke  and  Higashinaka, Ryuichiro  and  Asano, Hisako  and  Matsuo, Yoshihiro},
  title     = {A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences},
  booktitle = {Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  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.},
  url       = {http://aclweb.org/anthology/W16-4403}
}