Skip to main content
DORAS
DCU Online Research Access Service
Login (DCU Staff Only)
Identifying temporality of word senses based on minimum cuts

Hasanuzzaman, Mohammed ORCID: 0000-0003-1838-0091, Dias, Gaël, Ferrari, Stéphane, Mathet, Yann and Way, Andy ORCID: 0000-0001-5736-5930 (2016) Identifying temporality of word senses based on minimum cuts. In: CoNLL 2016:The SIGNLL Conference on Computational Natural Language Learning, 11-12 Aug 2016, Berlin, Germany. ISBN 978-1-945626-19-7

Full text available as:

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
141kB

Abstract

The ability to capture time information is essential to many natural language processing and information retrieval applications. Therefore, a lexical resource associating word senses to their temporal orientation might be crucial for the computational tasks aiming at the interpretation of language of time in texts. In this paper, we propose a semi-supervised minimum cuts strategy that makes use of WordNet glosses and semantic relations to supplement WordNet entries with temporal information. Intrinsic and extrinsic evaluations show that our approach outperforms prior semi-supervised non-graph classifiers.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of CoNLL 2016. . Association for Computational Linguistics. ISBN 978-1-945626-19-7
Publisher:Association for Computational Linguistics
Official URL:https://doi.org/10.18653/v1/K16-2
Copyright Information:© 2016 ACM
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:ADAPT Centre for Digital Content Technology is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
ID Code:23235
Deposited On:02 May 2019 12:30 by Thomas Murtagh . Last Modified 04 Jan 2021 16:55

Downloads

Downloads per month over past year

Archive Staff Only: edit this record

Altmetric
- Altmetric
+ Altmetric
  • Student Email
  • Staff Email
  • Student Apps
  • Staff Apps
  • Loop
  • Disclaimer
  • Privacy
  • Contact Us