{"id":3743,"url":"https://github.com/kobkrit/nlp_thai_resources","name":"nlp_thai_resources","description":"More than 50+ collections of Thai Natural Language Processing libraries. Update daily.","projects_count":59,"last_synced_at":"2026-10-07T05:00:19.976Z","repository":{"id":46226289,"uuid":"94662448","full_name":"kobkrit/nlp_thai_resources","owner":"kobkrit","description":"More than 50+ collections of Thai Natural Language Processing libraries. 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RNN. | Python | 93% F-measure. | MIT | Pucktada, [GitHub](https://github.com/pucktada/cutkum)\nCutThai | Thai word segmentation written in coffee-script Edit | Coffee-script | | MIT | Pureexe/cutthai [GitHub](https://github.com/pureexe/cutthai)\nDeepCut | A Thai word tokenization library using Deep Neural Network. CNN. | Python | 98.8% F-measure. | MIT | rkcosmos, [GitHub](https://github.com/rkcosmos/deepcut)\nLexto: Thai Lexeme Tokenizer | Java | | LGPL | [NECTEC](http://www.sansarn.com/lexto/license-lexto.php)\nLexto | Python 2 | | LGPL | [GitHub](https://github.com/Remixman/PythonLexTo)\nLexto | Python 3 | | LGPL | [GitHub](https://github.com/c4n/PythonLexTo)\nMulti-Candidate-Word-Segmentation | Multi Candidate Word Segmentation for Thai language | Python, RNN, LSTM | 97.0% F-measure (Word Level), 98.95% F-measure (Boundary Level) | MIT | [paper](https://goo.gl/hdhbiR), [GitHub](https://github.com/earthy123/Multi-Candidate-Word-Segmentation)\nPyThaiNLP | Python 3 | Maximal matching and various other engines | Apache 2.0 | [GitHub](https://github.com/PyThaiNLP/pythainlp)\nSwath | SWATH (Smart Word Analysis for THai) is a word segmentation for Thai | C | Longest Matching, Maximal Matching and Part-of-Speech Bigram. | GPL | Paisarn Charoenpornsawat, [CMU](http://www.cs.cmu.edu/%7Epaisarn/software.html)\nSynThai | Thai Word Segmentation and Part-of-Speech Tagging with Deep Learning. RNN. LSTM. | Python | 99.2% F-measure | MIT | KenjiroAI, [GitHub](https://github.com/KenjiroAI/SynThai)\nThai Language Toolkit (tltk) | Based on a paper by Wirote Aroonmanakun in 2002. Word segmentation is based on a maximum collocation approach. Syllable segmentation is based on 3grams statistics. (Dataset is included) | Python | 97.86% F-measure. (It was tested on a different testset; it is not fair to compare it with other models.) | GPLv3 | [PyPI](https://pypi.python.org/pypi/tltk/)\nWordcut | Thai word breaker for Node.js | JavaScript, Node.JS | | LGPL-3.0 | veer66, [GitHub](https://github.com/veer66/wordcut)\nwordcutpy | A simple Thai word tokenizer written in 1 Python file | Python 3 | | LGPL-3.0 | veer66, [GitHub](https://github.com/veer66/wordcutpy)\n\n### Part of Speech Tagging (POS Tagging)\nLibrary | Description | Programming Languages | Features | License | Author \u0026 Link\n--- | --- | --- | --- | --- | ---\nChart-POS | Thai POS Tagger | C | | All rights reserved | AIAT, KINDML, Thanaruk T. (thanaruk@siit.tu.ac.th), tchayintr, [Demo at iApp](https://ai.iapp.co.th/th-pos)\nJitar+NAiST | A simple Trigram HMM part-of-speech tagger | Java | | | [Ver66](https://veer66.wordpress.com/2012/03/20/part-of-speech-tagger-%E0%B8%AA%E0%B8%B3%E0%B8%AB%E0%B8%A3%E0%B8%B1%E0%B8%9A%E0%B8%A0%E0%B8%B2%E0%B8%A9%E0%B8%B2%E0%B9%84%E0%B8%97%E0%B8%A2/), [Jitar](https://github.com/danieldk/jitar) + [NAiST, 1](http://naist.cpe.ku.ac.th/pkg/jitar_model_large.zip) + [NAiST, 2](http://naist.cpe.ku.ac.th/pkg/jitar-20100224.zip)\nSynThai| Thai Word Segmentation and Part-of-Speech Tagging with Deep Learning. RNN. LSTM. | Python | 0.9163 F-measure. RNN. LSTM | MIT | [KenjiroAI, github](https://github.com/KenjiroAI/SynThai)\n\n### Name Entity Recognition\nLibrary | Description | Programming Languages | Features | License | Author \u0026 Link\n--- | --- | --- | --- | --- | ---\nNamed Entity Tagging (Thai NEST) | Thai Named Entity tagging Specification and Tools | | | GPL | [KINDML, SIIT](http://saki.siit.tu.ac.th/kindml/thainest/), [AIAT](http://aiat.in.th/resources/index.php/tools/48-1-0-named-entity-tagging-program-version-1-0)\nThaiNER | Thai Named Entity Recognition for PyThaiNLP | Python | | Apache 2.0 (code) \u0026 CC BY 3.0 (Dataset) | [ThaiNER](https://github.com/wannaphongcom/thai-ner)\n\n### News Structure Tagging\nLibrary | Description | Programming Languages | Features | License | Author \u0026 Link\n--- | --- | --- | --- | --- | ---\nNews Structure Tagging Program | Thai News Structure Tagging Program | | Metadata tagging, Structure tagging, Automatic News Title Generation | GPL | [AIAT](http://aiat.in.th/resources/index.php/tools/47-1-0-news-structure-tagging-program-version-1-0)\n\n### Syntactic Parsing \u0026 Tools\nLibrary | Description | Programming Languages | Features | License | Author \u0026 Link\n--- | --- | --- | --- | --- | ---\nChart-parser | Extract Syntactic Structure from POS Tagged Sentence. | C | | All rights reserved | AIAT, KINDML, Thanaruk T. (thanaruk@siit.tu.ac.th), tchayintr, [Demo at iApp](https://ai.iapp.co.th/th-parsing)\nGrammar Processing | Labelled Brackets -\u003e Context Free Grammars (CFGs) | Python | Transform and compute probability | | [tchayintr](https://github.com/tchayintr/nlp-python/tree/master/grammar_processing)\n\n### Word Embedding\nLibrary | Description | Programming Languages | Features | License | Author \u0026 Link\n--- | --- | --- | --- | --- | ---\nkobkrit-word-embedding | Tensorflow implementation of Thai word embedding | Python | Source code, Example, Word distance graph | LGPL | [Kobkrit V.](https://github.com/kobkrit/tf-nlp-thai-word-embedding)\n\n### Question Answering (Machine Comprehension)\nService | Description | License | Author \u0026 Link\n--- | --- | --- | ---\nThai Machine Comprehension (ThaiMC) | Bidirectional Attention Flow | Copyright (As the service) | [iApp-AI](http://ai.iapp.co.th/)\n\n### Emojification\nService | Description | License | Author \u0026 Link\n--- | --- | --- | ---\nThai Emotification | LSTM | GPL | [Demo at iApp-AI](http://ai.iapp.co.th/emojify) and [Source, Github](https://github.com/kobkrit/thai-emojification)\n\n\n## Corpus and Dataset\n\n### Dictionaries / Translation Pairs\nLibrary | Description | Size | Features | License | Link\n--- | --- | --- | --- | --- | ---\nLEXiTRON | Thai\u003c-\u003eEnglish Dictionary | | TH-\u003eEN, EN-\u003eTH | LEXiTRON License | [NECTEC](http://www.sansarn.com/lexto/license-lexitron.php)\nTransliteration Corpus | | 31K pairs | Thai-Eng Translation Pair | CC BY-NC-SA 3.0 TH | [NECTEC](https://www.nectec.or.th/corpus/index.php?league=pm)\nYaitron | LEXiTRON in machine readable format (XML) | | TH-\u003eEN, EN-\u003eTH | LEXiTRON License | Veer66 [Schema](http://th.lug.wikia.com/wiki/YAiTRON_%28English-Thai_dictionary%29), [Data \u0026 Conversion Code](https://github.com/veer66/Yaitron)\n\n### Downloadable Text Corpus\n\nLibrary | Description | Size | Features | License | Link\n--- | --- | --- | --- | --- | ---\nClick Bait Sentences | Thai Click Bait Sentence | 330 sent. (90.7KB) | | MIT | [Wannaphongcom](https://github.com/wannaphongcom/lexicon-thai/blob/master/clickbait/1.txt)\nInterBEST 2009/2010 | | 5M words | Word Seg. | CC BY-NC-SA 3.0 TH | [NECTEC](https://www.nectec.or.th/corpus/index.php?league=pm)\nORCHID | | 30K sent. | Word Seg., POS Tagged. | CC BY-NC-SA 3.0 TH | [NECTEC](https://www.nectec.or.th/corpus/index.php?league=pm)\nPrime Minister 29 | Prime Minister 29's Speech Sentences | 338KB | Word segged, Name Entity Tagged | MIT | [Wannaphongcom](https://github.com/wannaphongcom/lexicon-thai/tree/master/thai-corpus/Prime%20Minister%2029)\nthai-jokes-corpus | Cleaned Thai Jokes Corpus | 457 jokes | | GPLv3 | [iApp Technology](https://github.com/iapp-technology/thai-joke-corpus)\nThai named entity corpora | named entity corpora by Wirote Aroonmanakun's students | 266KB-1.5MB | syllable seg., word seg., Named Entity tagged | GPLv3 (not sure, but tltk is using this license) | [นัชชา ถิระสาโรช Data](http://pioneer.chula.ac.th/~awirote/Data-Nutcha.zip)\u003cbr /\u003e [ศศิวิมล กาลันสีมา Data](http://pioneer.chula.ac.th/~awirote/Data-Sasiwimon.zip)\u003cbr /\u003e [ณัฐดาพร เลิศชีวะ Data](http://pioneer.chula.ac.th/~awirote/Data-Nattadaporn.zip)\nTHAI-NEST | Thai-NEST: Thai Named Entity tagging Specification and Tools | 45K+ Name Entity Token | Name Entity Tagged | LGPL | [KINDML](https://saki.siit.tu.ac.th/kindml/thainest/index.php/download)\nThai Sentimental Word List | Thai Sentimental Words List | 52KB | Seperated Words as Adj, V | MIT | [Wannaphongcom](https://github.com/wannaphongcom/lexicon-thai/tree/master/sentiment)\nThai Wikipedia | Formal Articles | 1.49GB (~213.1 MB compressed) | XML | GFDL | [WIKIPEDIA](https://dumps.wikimedia.org/thwiki/latest/thwiki-latest-pages-articles.xml.bz2)\nThai WordNet | [THE CONSTRUCTION OF THAI WORDNET OF 1ST ORDER ENTITY COMMON BASE CONCEPTS USING A BI-DIRECTIONAL TRANSLATION METHOD AND WITH DICTIONARIES OF DIFFERENT COMPILATIONAL APPROACHES(ธนนท์ หลีน้อย)](http://www.arts.chula.ac.th/~ling/thesis/2551MA-DHANON.pdf) \u003cbr /\u003e \u003cbr /\u003e [THE CONSTRUCTION OF THAI WORDNET OF 2ND ORDER ENTITY COMMON BASE CONCEPTS USING A BI-DIRECTIONAL TRANSLATION METHOD : A STUDY OF THE DIVERSITY OF MEANINGS AFFECTING TRANSLATIONAL ACCURACY (ปริศนา อัครพุทธิพร)](http://cuir.car.chula.ac.th/handle/123456789/14274)||WordNet|N/A| [ธนนท์ หลีน้อย 2008](http://pioneer.chula.ac.th/~awirote/Data-Dhannon.rar)\u003cbr /\u003e[ปริศนา อัครพุทธิพร Data 2008](http://pioneer.chula.ac.th/~awirote/Data-Prisana.rar)\nTNC Top-5000 Words | Word frequency | 5,000 words | Frequency of Thai words in various genres, EXCEL | All rights reserved | [CHULA](http://www.arts.chula.ac.th/~ling/TNC/category.php?id=58\u0026)\nToxicity in Thai Tweet Corpus |Tokyo Metropolitan University Natural Language Processing Group | | Each tweet is labeled as toxic or non-toxic |CC BY-NC 4.0 | [tmu-nlp](https://github.com/tmu-nlp/ThaiToxicityTweetCorpus)\nWisesight Sentiment Corpus | Social media message with sentiment label (positive, neutral, negative, question). | ~26,700 messages | Sentiment label, Question label | Public domain | [PyThaiNLP](https://github.com/PyThaiNLP/wisesight-sentiment/)\n\n### Web Query Text Corpus\nLibrary | Description | Size | Features | License | Link\n--- | --- | --- | --- | --- | ---\nThai National Corpus 2 | | 32M words | Query text by genre, domain | All rights reserved | [CHULA](http://www.arts.chula.ac.th/~ling/TNCII/)\nThai Medical Document | | 3,594 docs | Document and dynamic keyword map | All rights reserved | [KINDML, SIIT](http://md.knowdd.com/)\nSoutheast Asian Languages Library | Thai News, Web Text, Pop Music, Literature, Toponyms | 20M chars | Phase around a search text | | [SEALang](http://sealang.net/thai/corpus.htm)\nHSE Thai Corpus | Modern texts written in Thai language (mostly news websites) | 50M tokens | Query by word form, lexeme, translation, grammatical attributes, lexical attributees | | [HSE School of Linguistics](http://web-corpora.net/ThaiCorpus/search/)\n\n### Parallel Corpus\nLibrary | Description | Size | Features | License | Link\n--- | --- | --- | --- | --- | ---\nTALPCo | TUFS Asian Language Parallel Corpus | 1327 sent | open parallel corpus consisting of Japanese sentences and their translations into Burmese (Myanmar; the official language of the Republic of the Union of Myanmar), Malay (the national language of Malaysia, Singapore and Brunei), Indonesian, Thai, Vietnamese and English | CC BY 4.0 | [TALPCo](https://github.com/matbahasa/TALPCo)\n\n\n## Pre-trained Language Models\nPre-trained Model | Description | Size | Dimensions | License | Link\n--- | --- | --- | --- | --- | ---\nfastText | Skip-Gram model trained on Wikipedia using fastText | | 300 | CC BY-SA 3.0 | [Facebook](https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md) + [Bin \u0026 Text](https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.th.zip) + [Text Only](https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.th.vec)\nthai2fit | ULMFit on Wikipedia. Perplexity of 46.80959 with 60,002 embeddings. | 70MB | 300 | MIT | [thai2vec](https://github.com/cstorm125/thai2fit) / [PyThaiNLP](https://github.com/PyThaiNLP/)\nthbert | Yet another pre-trained BERT particularly in Thai | | | Apache 2.0 | [tchayintr](https://github.com/tchayintr/thbert)\n\n## Benchmarks\n\n[Thai Text Classification Benchmarks](https://www.github.com/PyThaiNLP/classification-benchmarks)\n- [wongnai-corpus](https://github.com/wongnai/wongnai-corpus)\n- [prachathai-67k](https://github.com/PyThaiNLP/prachathai-67k)\n- [wisesight-sentiment](https://github.com/PyThaiNLP/wisesight-sentiment)\n- [truevoice-intent](https://github.com/PyThaiNLP/truevoice-intent): destination\n\n\n## Tools\n\n### Corpus extractors\n\nLibrary | Description | Programming Languages | Features | License | Author \u0026 Link\n--- | --- | --- | --- | --- | ---\nBEST2010 cooker | A tool for extracting segmented words from Thai segmented BEST2010 corpus | Python3 | Extracting segmented words, features, and data divisions | Apache 2.0 | [tchayintr](https://github.com/tchayintr/best2010_cooker)\n\n## Not found? Try to look at another Thai NLP Awesome List/Resource (Like this one)\nhttps://resources.aiat.or.th/\n\n\n## Acknowledgements\n* [bact](https://github.com/kobkrit/nlp_thai_resources/commits/master/README.md?author=bact) - For suggestions on license words.\n* [C4N](https://github.com/kobkrit/nlp_thai_resources/commits/master/README.md?author=c4n)\n* [Veer66](https://github.com/kobkrit/nlp_thai_resources/commits/master/README.md?author=veer66)\n* [Bi89](https://github.com/kobkrit/nlp_thai_resources/commits/master/README.md?author=bi89)\n* [Tchayintr](https://github.com/kobkrit/nlp_thai_resources/commits/master/README.md?author=tchayintr)\n* [PureEXE](https://github.com/kobkrit/nlp_thai_resources/commits/master/README.md?author=pureexe)\n* [Cstorm125](https://github.com/kobkrit/nlp_thai_resources/commits/master/README.md?author=cstorm125)\n* [Wannaphongcom](https://github.com/kobkrit/nlp_thai_resources/commits/master/README.md?author=wannaphongcom)\n* [Ekapolc](https://github.com/kobkrit/nlp_thai_resources/commits/master/README.md?author=ekapolc)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/kobkrit%2Fnlp_thai_resources/projects"}