BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern- and Graph-based Information to Identify Discriminative Attributes

Enrico Santus, Chris Biemann, Emmanuele Chersoni

Abstract
This paper describes BomJi, a supervised system for capturing discriminative attributes in word pairs (e.g. yellow as discriminative for banana over watermelon). The system relies on an XGB classifier trained on carefully engineered graph-, pattern- and word embedding-based features. It participated in the SemEval-2018 Task 10 on Capturing Discriminative Attributes, achieving an F1 score of 0.73 and ranking 2nd out of 26 participant systems.
Anthology ID:
S18-1163
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:
990–994
Language:
URL:
https://aclanthology.org/S18-1163
DOI:
10.18653/v1/S18-1163
Bibkey:
Cite (ACL):
Enrico Santus, Chris Biemann, and Emmanuele Chersoni. 2018. BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern- and Graph-based Information to Identify Discriminative Attributes. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 990–994, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
BomJi at SemEval-2018 Task 10: Combining Vector-, Pattern- and Graph-based Information to Identify Discriminative Attributes (Santus et al., SemEval 2018)
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PDF:
https://preview.aclanthology.org/proper-vol2-ingestion/S18-1163.pdf
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