SUNNYNLP at SemEval-2018 Task 10: A Support-Vector-Machine-Based Method for Detecting Semantic Difference using Taxonomy and Word Embedding Features

Sunny Lai, Kwong Sak Leung, Yee Leung


Abstract
We present SUNNYNLP, our system for solving SemEval 2018 Task 10: “Capturing Discriminative Attributes”. Our Support-Vector-Machine(SVM)-based system combines features extracted from pre-trained embeddings and statistical information from Is-A taxonomy to detect semantic difference of concepts pairs. Our system is demonstrated to be effective in detecting semantic difference and is ranked 1st in the competition in terms of F1 measure. The open source of our code is coined SUNNYNLP.
Anthology ID:
S18-1118
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:
741–746
Language:
URL:
https://aclanthology.org/S18-1118/
DOI:
10.18653/v1/S18-1118
Bibkey:
Cite (ACL):
Sunny Lai, Kwong Sak Leung, and Yee Leung. 2018. SUNNYNLP at SemEval-2018 Task 10: A Support-Vector-Machine-Based Method for Detecting Semantic Difference using Taxonomy and Word Embedding Features. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 741–746, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
SUNNYNLP at SemEval-2018 Task 10: A Support-Vector-Machine-Based Method for Detecting Semantic Difference using Taxonomy and Word Embedding Features (Lai et al., SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1118.pdf
Code
 Yermouth/sunnynlp
Data
YAGO