# Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks

arXiv: https://arxiv.org/abs/1704.07616 · HTML: https://mlsanity.com/paper/1704.07616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2017.

## Built on by (most cited first, 1 of 1)

- [Building Foundations for Natural Language Processing of Historical Turkish: Resources and Models](https://mlsanity.com/paper/2501.04828/) (2025)

## Abstract (arXiv)

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.