@inproceedings{43f09f6af35d4ae0b8414526af0e4a27,
title = "Relation extraction: Perspective from convolutional neural networks",
abstract = "Up to now, relation extraction systems have made extensive use of features generated by linguistic analysis modules. Errors in these features lead to errors of relation detection and classification. In this work, we depart from these traditional approaches with complicated feature engineering by introducing a convolutional neural network for relation extraction that automatically learns features from sentences and minimizes the dependence on external toolkits and resources. Our model takes advantages of multiple window sizes for filters and pre-trained word embeddings as an initializer on a non-static architecture to improve the performance. We emphasize the relation extraction problem with an unbalanced corpus. The experimental results show that our system significantly outperforms not only the best baseline systems for relation extraction but also the state-of-the-art systems for relation classification.",
author = "Nguyen, {Thien Huu} and Ralph Grishman",
note = "Publisher Copyright: {\textcopyright} 2015 The North American Chapter of the Association for Computational Linguistics.; 1st Workshop on Vector Space Modeling for Natural Language Processing, VS 2015 at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015 ; Conference date: 05-06-2015",
year = "2015",
language = "English (US)",
series = "1st Workshop on Vector Space Modeling for Natural Language Processing, VS 2015 at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2015",
publisher = "Association for Computational Linguistics (ACL)",
pages = "39--48",
editor = "Phil Blunsom and Shay Cohen and Paramveer Dhillon and Percy Liang",
booktitle = "1st Workshop on Vector Space Modeling for Natural Language Processing, VS 2015 at the Conference of the North American Chapter of the Association for Computational Linguistics",
}