@inproceedings{55c634c53a8a435bb791cf77e6a41f1c,
title = "Employing word representations and regularization for domain adaptation of relation extraction",
abstract = "Relation extraction suffers from a performance loss when a model is applied to out-of-domain data. This has fostered the development of domain adaptation techniques for relation extraction. This paper evaluates word embeddings and clustering on adapting feature-based relation extraction systems. We systematically explore various ways to apply word embeddings and show the best adaptation improvement by combining word cluster and word embedding information. Finally, we demonstrate the effectiveness of regularization for the adaptability of relation extractors.",
author = "Nguyen, {Thien Huu} and Ralph Grishman",
note = "Funding Information: We thank the anonymous reviewers for their helpful feedback, which substantially improved the letter. Part of this work was carried out while J.D. was at the Laboratory for Information and Decision Systems, MIT, Cambridge, MA.; 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 ; Conference date: 22-06-2014 Through 27-06-2014",
year = "2014",
doi = "10.3115/v1/p14-2012",
language = "English (US)",
isbn = "9781937284732",
series = "52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "68--74",
booktitle = "Long Papers",
}