Abstract
Semantic difference detection attempts to capture whether a word is a discriminative attribute between two other words. For example, the discriminative feature red characterizes the first word from the (apple, banana) pair, but not the second. Modeling semantic difference is essential for language understanding systems, as it provides useful information for identifying particular aspects of word senses. This paper describes our system implementation (the ALB system of the NLP@Unibuc team) for the 10th task of the SemEval 2018 workshop, “Capturing Discriminative Attributes”. We propose a method for semantic difference detection that uses an SVM classifier with features based on co-occurrence counts and shallow semantic parsing, achieving 0.63 F1 score in the competition.- Anthology ID:
- S18-1158
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venues:
- SemEval | *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 963–967
- Language:
- URL:
- https://aclanthology.org/S18-1158
- DOI:
- 10.18653/v1/S18-1158
- Cite (ACL):
- Bogdan Dumitru, Alina Maria Ciobanu, and Liviu P. Dinu. 2018. ALB at SemEval-2018 Task 10: A System for Capturing Discriminative Attributes. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 963–967, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- ALB at SemEval-2018 Task 10: A System for Capturing Discriminative Attributes (Dumitru et al., SemEval-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S18-1158.pdf