An Improved Neural Network Model for Joint POS Tagging and Dependency Parsing

Dat Quoc Nguyen, Karin Verspoor


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/K18-2008.pdf
Code
 datquocnguyen/jPTDP
Data
Penn Treebank