Abstract
We present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier. We trained our transition-based projective parser in UD version 2.0 datasets without any additional data. The parser is fast, lightweight and effective on big treebanks. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, the official results show that the macro-averaged LAS F1 score of our system Mengest is 61.33%.- Anthology ID:
- K17-3025
- Volume:
- Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Jan Hajič, Dan Zeman
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 237–242
- Language:
- URL:
- https://aclanthology.org/K17-3025
- DOI:
- 10.18653/v1/K17-3025
- Cite (ACL):
- Tao Ji, Yuanbin Wu, and Man Lan. 2017. A Fast and Lightweight System for Multilingual Dependency Parsing. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 237–242, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Fast and Lightweight System for Multilingual Dependency Parsing (Ji et al., CoNLL 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/K17-3025.pdf