Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks
Computation and Language
2017-04-26 v1
Abstract
While part-of-speech (POS) tagging and dependency parsing are observed to be closely related, existing work on joint modeling with manually crafted feature templates suffers from the feature sparsity and incompleteness problems. In this paper, we propose an approach to joint POS tagging and dependency parsing using transition-based neural networks. Three neural network based classifiers are designed to resolve shift/reduce, tagging, and labeling conflicts. Experiments show that our approach significantly outperforms previous methods for joint POS tagging and dependency parsing across a variety of natural languages.
Cite
@article{arxiv.1704.07616,
title = {Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks},
author = {Liner Yang and Meishan Zhang and Yang Liu and Nan Yu and Maosong Sun and Guohong Fu},
journal= {arXiv preprint arXiv:1704.07616},
year = {2017}
}