Abstract
This paper describes a system submitted to the closed track of the PARSEME shared task (edition 1.1) on automatic identification of verbal multiword expressions (VMWEs). The system represents VMWE identification as a labeling task where one of two labels (MWE or not-MWE) must be predicted for each node in the dependency tree based on local context, including adjacent nodes and their labels. The system relies on multiclass logistic regression to determine the globally optimal labeling of a tree. The system ranked 1st in the general cross-lingual ranking of the closed track systems, according to both official evaluation measures: MWE-based F1 and token-based F1.- Anthology ID:
- W18-4931
- Volume:
- Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Agata Savary, Carlos Ramisch, Jena D. Hwang, Nathan Schneider, Melanie Andresen, Sameer Pradhan, Miriam R. L. Petruck
- Venues:
- LAW | MWE
- SIGs:
- SIGLEX | SIGANN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 275–282
- Language:
- URL:
- https://aclanthology.org/W18-4931
- DOI:
- Cite (ACL):
- Jakub Waszczuk. 2018. TRAVERSAL at PARSEME Shared Task 2018: Identification of Verbal Multiword Expressions Using a Discriminative Tree-Structured Model. In Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018), pages 275–282, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- TRAVERSAL at PARSEME Shared Task 2018: Identification of Verbal Multiword Expressions Using a Discriminative Tree-Structured Model (Waszczuk, LAW-MWE 2018)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W18-4931.pdf