Abstract
This paper presents a comparison of several approaches for capturing discriminative attributes and considers an impact of concatenation of several word embeddings of different nature on the classification performance. A similarity-based method is proposed and compared with classical machine learning approaches. It is shown that this method outperforms others on all the considered word vector models and there is a performance increase when concatenated datasets are used.- Anthology ID:
- S18-1164
- 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:
- 995–998
- Language:
- URL:
- https://aclanthology.org/S18-1164
- DOI:
- 10.18653/v1/S18-1164
- Cite (ACL):
- Maxim Grishin. 2018. Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 995–998, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Igevorse at SemEval-2018 Task 10: Exploring an Impact of Word Embeddings Concatenation for Capturing Discriminative Attributes (Grishin, SemEval 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/S18-1164.pdf