# ThaiTrees: Thai Syntactic Dependency Trees Across Domains

arXiv: https://arxiv.org/abs/2609.27558 · HTML: https://mlsanity.com/paper/2609.27558/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [PyThaiNLP: Thai Natural Language Processing in Python](https://mlsanity.com/paper/2312.04649/) (2023)
- [WangchanBERTa: Pretraining transformer-based Thai Language Models](https://mlsanity.com/paper/2101.09635/) (2021)
- [Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl](https://mlsanity.com/paper/1710.01779/) (2017)
- [The Annotation Guideline of LST20 Corpus](https://mlsanity.com/paper/2008.05055/) (2020)

## Abstract (arXiv)

Studying syntactic patterns in naturally occurring language requires a large parsed corpus, but manual annotation is costly and difficult to scale. Thai has a manually annotated dependency treebank for training and evaluating parsers, but lacks a large automatically parsed corpus for quantitative syntactic research. We present ThaiTrees, a 342M-token corpus drawn from news, Wikipedia, spoken transcripts, and social media. We develop a reproducible pipeline for cleaning, processing, and parsing Thai text under the Universal Dependencies framework. The resulting corpus makes grammatical relations searchable and supports the study of syntactic distributions. We release a frequency lexicon and CoNLL-U parses in machine-readable formats suitable for both AI-assisted and conventional programmatic analysis.