ThaiTrees: Thai Syntactic Dependency Trees Across Domains
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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.
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References · 5
- 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
- 2023PyThaiNLP: Thai Natural Language Processing in Pythoncited by 28
- 2021WangchanBERTa: Pretraining transformer-based Thai Language Modelscited by 26
- 2017Building a Web-Scale Dependency-Parsed Corpus from CommonCrawlcited by 9
- 2020The Annotation Guideline of LST20 Corpuscited by 4
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