2 research outputs found
A neural joint model for Vietnamese word segmentation, POS tagging and dependency parsing
We propose the first multi-task learning model for joint Vietnamese word
segmentation, part-of-speech (POS) tagging and dependency parsing. In
particular, our model extends the BIST graph-based dependency parser
(Kiperwasser and Goldberg, 2016) with BiLSTM-CRF-based neural layers (Huang et
al., 2015) for word segmentation and POS tagging. On Vietnamese benchmark
datasets, experimental results show that our joint model obtains
state-of-the-art or competitive performances.Comment: In Proceedings of the 17th Annual Workshop of the Australasian
Language Technology Association (ALTA 2019
Vietnamese transition-based dependency parsing with supertag features
In recent years, dependency parsing is a fascinating research topic and has a
lot of applications in natural language processing. In this paper, we present
an effective approach to improve dependency parsing by utilizing supertag
features. We performed experiments with the transition-based dependency parsing
approach because it can take advantage of rich features. Empirical evaluation
on Vietnamese Dependency Treebank showed that, we achieved an improvement of
18.92% in labeled attachment score with gold supertags and an improvement of
3.57% with automatic supertags.Comment: 2016 Eighth International Conference on Knowledge and Systems
Engineering (KSE