UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes

Milton King, Ali Hakimi Parizi, Paul Cook

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Abstract
In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model.
Anthology ID:
S18-1168
Volume:
Proceedings of the 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1013–1016
Language:
URL:
https://aclanthology.org/S18-1168
DOI:
10.18653/v1/S18-1168
Bibkey:
Cite (ACL):
Milton King, Ali Hakimi Parizi, and Paul Cook. 2018. UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1013–1016, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes (King et al., SemEval 2018)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/S18-1168.pdf
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