Abstract
The paper describes our system submitted for the Workshop on Multiword Expressions’ shared task on automatic identification of verbal multiword expressions. It uses POS tagging and dependency parsing to identify single- and multi-token verbal MWEs in text. Our system is language independent and competed on nine of the eighteen languages. Our paper describes how our system works and gives its error analysis for the languages it was submitted for.- Anthology ID:
- W17-1705
- Volume:
- Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Stella Markantonatou, Carlos Ramisch, Agata Savary, Veronika Vincze
- Venue:
- MWE
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 48–53
- Language:
- URL:
- https://aclanthology.org/W17-1705
- DOI:
- 10.18653/v1/W17-1705
- Cite (ACL):
- Katalin Ilona Simkó, Viktória Kovács, and Veronika Vincze. 2017. USzeged: Identifying Verbal Multiword Expressions with POS Tagging and Parsing Techniques. In Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017), pages 48–53, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- USzeged: Identifying Verbal Multiword Expressions with POS Tagging and Parsing Techniques (Simkó et al., MWE 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-1705.pdf