Abstract
We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+% absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental results on parsing 61 “big” Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Strakova, 2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at: https://github.com/datquocnguyen/jPTDP- Anthology ID:
- K18-2008
- Volume:
- Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Daniel Zeman, Jan Hajič
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 81–91
- Language:
- URL:
- https://aclanthology.org/K18-2008
- DOI:
- 10.18653/v1/K18-2008
- Cite (ACL):
- Dat Quoc Nguyen and Karin Verspoor. 2018. An Improved Neural Network Model for Joint POS Tagging and Dependency Parsing. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 81–91, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- An Improved Neural Network Model for Joint POS Tagging and Dependency Parsing (Nguyen & Verspoor, CoNLL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/K18-2008.pdf
- Code
- datquocnguyen/jPTDP
- Data
- Penn Treebank