@inproceedings{3838e6bd88194c0287c3d76663a68271,
title = "Event detection and domain adaptation with convolutional neural networks",
abstract = "We study the event detection problem us-ing convolutional neural networks (CNNs) that overcome the two fundamental limi-tations of the traditional feature-based ap-proaches to this task: complicated feature engineering for rich feature sets and er-ror propagation from the preceding stages which generate these features. The experi-mental results show that the CNNs outper-form the best reported feature-based sys-tems in the general setting as well as the domain adaptation setting without resort-ing to extensive external resources.",
author = "Nguyen, {Thien Huu} and Ralph Grishman",
note = "Publisher Copyright: {\textcopyright} 2015 Association for Computational Linguistics.; 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015 ; Conference date: 26-07-2015 Through 31-07-2015",
year = "2015",
doi = "10.3115/v1/p15-2060",
language = "English (US)",
series = "ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "365--371",
booktitle = "ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference",
}