@inproceedings{garg-etal-2019-nearly,
title = "Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction",
author = "Garg, Sahil and
Galstyan, Aram and
Ver Steeg, Greg and
Cecchi, Guillermo",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1414",
doi = "10.18653/v1/D19-1414",
pages = "4026--4036",
abstract = "Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks. In this paper, we propose to optimize the hashcode representations in a nearly unsupervised manner, in which we only use data points, but not their class labels, for learning. The optimized hashcode representations are then fed to a supervised classifi er following the prior work. This nearly unsupervised approach allows fine-grained optimization of each hash function, which is particularly suitable for building hashcode representations generalizing from a training set to a test set. We empirically evaluate the proposed approach for biomedical relation extraction tasks, obtaining significant accuracy improvements w.r.t. state-of-the-art supervised and semi-supervised approaches.",
}
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<abstract>Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks. In this paper, we propose to optimize the hashcode representations in a nearly unsupervised manner, in which we only use data points, but not their class labels, for learning. The optimized hashcode representations are then fed to a supervised classifi er following the prior work. This nearly unsupervised approach allows fine-grained optimization of each hash function, which is particularly suitable for building hashcode representations generalizing from a training set to a test set. We empirically evaluate the proposed approach for biomedical relation extraction tasks, obtaining significant accuracy improvements w.r.t. state-of-the-art supervised and semi-supervised approaches.</abstract>
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%0 Conference Proceedings
%T Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction
%A Garg, Sahil
%A Galstyan, Aram
%A Ver Steeg, Greg
%A Cecchi, Guillermo
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F garg-etal-2019-nearly
%X Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks. In this paper, we propose to optimize the hashcode representations in a nearly unsupervised manner, in which we only use data points, but not their class labels, for learning. The optimized hashcode representations are then fed to a supervised classifi er following the prior work. This nearly unsupervised approach allows fine-grained optimization of each hash function, which is particularly suitable for building hashcode representations generalizing from a training set to a test set. We empirically evaluate the proposed approach for biomedical relation extraction tasks, obtaining significant accuracy improvements w.r.t. state-of-the-art supervised and semi-supervised approaches.
%R 10.18653/v1/D19-1414
%U https://aclanthology.org/D19-1414
%U https://doi.org/10.18653/v1/D19-1414
%P 4026-4036
Markdown (Informal)
[Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction](https://aclanthology.org/D19-1414) (Garg et al., EMNLP-IJCNLP 2019)
ACL
- Sahil Garg, Aram Galstyan, Greg Ver Steeg, and Guillermo Cecchi. 2019. Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4026–4036, Hong Kong, China. Association for Computational Linguistics.