# Character-Level Chinese Dependency Parsing via Modeling Latent Intra-Word Structure

arXiv: https://arxiv.org/abs/2406.03772 · HTML: https://mlsanity.com/paper/2406.03772/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 6 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Efficient Second-Order TreeCRF for Neural Dependency Parsing](https://mlsanity.com/paper/2005.00975/) (2020)
- [Unsupervised Neural Hidden Markov Models](https://mlsanity.com/paper/1609.09007/) (2016)
- [Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments](https://mlsanity.com/paper/2110.06865/) (2021)
- [A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing](https://mlsanity.com/paper/1904.04697/) (2019)
- [An In-depth Study on Internal Structure of Chinese Words](https://mlsanity.com/paper/2106.00334/) (2021)

## Abstract (arXiv)

Revealing the syntactic structure of sentences in Chinese poses significant challenges for word-level parsers due to the absence of clear word boundaries. To facilitate a transition from word-level to character-level Chinese dependency parsing, this paper proposes modeling latent internal structures within words. In this way, each word-level dependency tree is interpreted as a forest of character-level trees. A constrained Eisner algorithm is implemented to ensure the compatibility of character-level trees, guaranteeing a single root for intra-word structures and establishing inter-word dependencies between these roots. Experiments on Chinese treebanks demonstrate the superiority of our method over both the pipeline framework and previous joint models. A detailed analysis reveals that a coarse-to-fine parsing strategy empowers the model to predict more linguistically plausible intra-word structures.