@inproceedings{tran-etal-2019-relation,
title = "Relation Classification Using Segment-Level Attention-based {CNN} and Dependency-based {RNN}",
author = "Tran, Van-Hien and
Phi, Van-Thuy and
Shindo, Hiroyuki and
Matsumoto, Yuji",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1286",
doi = "10.18653/v1/N19-1286",
pages = "2793--2798",
abstract = "Recently, relation classification has gained much success by exploiting deep neural networks. In this paper, we propose a new model effectively combining Segment-level Attention-based Convolutional Neural Networks (SACNNs) and Dependency-based Recurrent Neural Networks (DepRNNs). While SACNNs allow the model to selectively focus on the important information segment from the raw sequence, DepRNNs help to handle the long-distance relations from the shortest dependency path of relation entities. Experiments on the SemEval-2010 Task 8 dataset show that our model is comparable to the state-of-the-art without using any external lexical features.",
}
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%0 Conference Proceedings
%T Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN
%A Tran, Van-Hien
%A Phi, Van-Thuy
%A Shindo, Hiroyuki
%A Matsumoto, Yuji
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F tran-etal-2019-relation
%X Recently, relation classification has gained much success by exploiting deep neural networks. In this paper, we propose a new model effectively combining Segment-level Attention-based Convolutional Neural Networks (SACNNs) and Dependency-based Recurrent Neural Networks (DepRNNs). While SACNNs allow the model to selectively focus on the important information segment from the raw sequence, DepRNNs help to handle the long-distance relations from the shortest dependency path of relation entities. Experiments on the SemEval-2010 Task 8 dataset show that our model is comparable to the state-of-the-art without using any external lexical features.
%R 10.18653/v1/N19-1286
%U https://aclanthology.org/N19-1286
%U https://doi.org/10.18653/v1/N19-1286
%P 2793-2798
Markdown (Informal)
[Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN](https://aclanthology.org/N19-1286) (Tran et al., NAACL 2019)
ACL