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DCU: using distributional semantics and domain adaptation for the semantic textual similarity SemEval-2015 Task 2

Arora, Piyush orcid logoORCID: 0000-0002-4261-2860, Hokamp, Chris orcid logoORCID: 0000-0002-7850-9398, Foster, Jennifer orcid logoORCID: 0000-0002-7789-4853 and Jones, Gareth J.F. orcid logoORCID: 0000-0003-2923-8365 (2015) DCU: using distributional semantics and domain adaptation for the semantic textual similarity SemEval-2015 Task 2. In: International Workshop on Semantic Evaluation (SemEval 2015), 4-5 June 2015, Denver, Co. USA.

Abstract
We describe the work carried out by the DCU team on the Semantic Textual Similarity task at SemEval-2015. We learn a regression model to predict a semantic similarity score between a sentence pair. Our system exploits distributional semantics in combination with tried-and-tested features from previous tasks in order to compute sentence similarity. Our team submitted 3 runs for each of the five English test sets. For two of the test sets, belief and headlines, our best system ranked second and fourth out of the 73 submitted systems. Our best submission averaged over all test sets ranked 26 out of the 73 systems.
Metadata
Item Type:Conference or Workshop Item (Poster)
Event Type:Workshop
Refereed:Yes
Subjects:Computer Science > Computational linguistics
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Research Initiatives and Centres > Centre for Next Generation Localisation (CNGL)
Published in: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:http://dx.doi.org/10.18653/v1/S15-2026
Copyright Information:© 2015 Association for Computational Linguistics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland (SFI) as a part of the CNGL Centre for Global Intelligent Content at DCU (Grant No: 12/CE/I2267)
ID Code:22795
Deposited On:30 Nov 2018 10:22 by Piyush Arora . Last Modified 22 Jul 2019 14:02
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