Van-Hien Tran
2021
CovRelex: A COVID-19 Retrieval System with Relation Extraction
Vu Tran
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Van-Hien Tran
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Phuong Nguyen
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Chau Nguyen
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Ken Satoh
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Yuji Matsumoto
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Minh Nguyen
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
This paper presents CovRelex, a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers. This work aims at building a system supporting users efficiently in acquiring knowledge across a huge number of COVID-19 scientific papers published rapidly. Our system can be accessed via https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex/.
2019
Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN
Van-Hien Tran
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Van-Thuy Phi
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Hiroyuki Shindo
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Yuji Matsumoto
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)
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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Co-authors
- Yuji Matsumoto 2
- Van-Thuy Phi 1
- Hiroyuki Shindo 1
- Vu Tran 1
- Phuong Nguyen 1
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