Shimaoka, S;
Stenetorp, P;
Inui, K;
Riedel, S;
(2016)
An Attentive Neural Architecture for Fine-grained Entity Type Classification.
In:
Proceedings of the 5th Workshop on Automated Knowledge Base Construction (AKBC) at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Proceedings of the Workshop.
(pp. pp. 69-74).
Association for Computational Linguistics (ACL): San Diego, CA, USA.
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Abstract
In this work we propose a novel attentionbased neural network model for the task of fine-grained entity type classification that unlike previously proposed models recursively composes representations of entity mention contexts. Our model achieves state-of-theart performance with 74.94% loose micro F1- score on the well-established FIGER dataset, a relative improvement of 2.59% . We also investigate the behavior of the attention mechanism of our model and observe that it can learn contextual linguistic expressions that indicate the fine-grained category memberships of an entity.
Type: | Proceedings paper |
---|---|
Title: | An Attentive Neural Architecture for Fine-grained Entity Type Classification |
Event: | 5th Workshop on Automated Knowledge Base Construction (AKBC) |
ISBN-13: | 9781941643532 |
Open access status: | An open access version is available from UCL Discovery |
Publisher version: | http://aclweb.org/anthology/W/W16/ |
Language: | English |
Additional information: | Copyright © 2016 The Association for Computational Linguistics. This is an Open Access paper published under a Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/1503054 |
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