A POS Tagging Model Adapted to Learner English

Ryo Nagata, Tomoya Mizumoto, Yuta Kikuchi, Yoshifumi Kawasaki, Kotaro Funakoshi


Abstract
There has been very limited work on the adaptation of Part-Of-Speech (POS) tagging to learner English despite the fact that POS tagging is widely used in related tasks. In this paper, we explore how we can adapt POS tagging to learner English efficiently and effectively. Based on the discussion of possible causes of POS tagging errors in learner English, we show that deep neural models are particularly suitable for this. Considering the previous findings and the discussion, we introduce the design of our model based on bidirectional Long Short-Term Memory. In addition, we describe how to adapt it to a wide variety of native languages (potentially, hundreds of them). In the evaluation section, we empirically show that it is effective for POS tagging in learner English, achieving an accuracy of 0.964, which significantly outperforms the state-of-the-art POS-tagger. We further investigate the tagging results in detail, revealing which part of the model design does or does not improve the performance.
Anthology ID:
W18-6106
Volume:
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
Month:
November
Year:
2018
Address:
Brussels, Belgium
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
39–48
Language:
URL:
https://aclanthology.org/W18-6106
DOI:
10.18653/v1/W18-6106
Bibkey:
Cite (ACL):
Ryo Nagata, Tomoya Mizumoto, Yuta Kikuchi, Yoshifumi Kawasaki, and Kotaro Funakoshi. 2018. A POS Tagging Model Adapted to Learner English. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 39–48, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
A POS Tagging Model Adapted to Learner English (Nagata et al., WNUT 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-6106.pdf