PyThaiNLP: Thai Natural Language Processing in Python

PyThaiNLP is a Python package for text processing and linguistic analysis, similar to NLTK with a focus on Thai language.

PyThaiNLP เป็นไลบารีภาษาไพทอนสำหรับประมวลผลภาษาธรรมชาติ คล้ายกับ NLTK โดยเน้นภาษาไทย ดูรายละเอียดภาษาไทยได้ที่ README_TH.MD

Now, You can contact with or ask any questions of the PyThaiNLP team.

• PyThaiNLP 2 requires Python 3.7+. Python 2.7 users can use PyThaiNLP 1.6. See 2.0 change log | Upgrading from 1.7 | Upgrading ThaiNER from 1.7
• PyThaiNLP Get Started notebook | API document | Tutorials
• Official website | PyPI | Facebook page
• Who uses PyThaiNLP?
• Model cards - for technical details, caveats, and ethical considerations of the models developed and used in PyThaiNLP

PyThaiNLP provides standard linguistic analysis for Thai language and standard Thai locale utility functions. Some of these functions are also available via the command-line interface (run thainlp in your shell).

Partial list of features:

• Convenient character and word classes, like Thai consonants (pythainlp.thai_consonants), vowels (pythainlp.thai_vowels), digits (pythainlp.thai_digits), and stop words (pythainlp.corpus.thai_stopwords) -- comparable to constants like string.letters, string.digits, and string.punctuation
• Linguistic unit segmentation at different levels: sentence (sent_tokenize), word (word_tokenize), and subword (subword_tokenize)
• Part-of-speech tagging (pos_tag)
• Spelling suggestion and correction (spell and correct)
• Phonetic algorithm and transliteration (soundex and transliterate)
• Collation (sorted by dictionary order) (collate)
• Number read out (num_to_thaiword and bahttext)
• Datetime formatting (thai_strftime)
• Thai-English keyboard misswitched fix (eng_to_thai, thai_to_eng)

pip install --upgrade pythainlp

This will install the latest stable release of PyThaiNLP.

Install different releases:

• Stable release: pip install --upgrade pythainlp
• Pre-release (nearly ready): pip install --upgrade --pre pythainlp
• Development (likely to break things): pip install https://github.com/PyThaiNLP/pythainlp/archive/dev.zip

Some functionalities, like Thai WordNet, may require extra packages. To install those requirements, specify a set of [name] immediately after pythainlp:

pip install pythainlp[extra1,extra2,...]

Possible extras:

• full (install everything)
• attacut (to support attacut, a fast and accurate tokenizer)
• benchmarks (for word tokenization benchmarking)
• icu (for ICU, International Components for Unicode, support in transliteration and tokenization)
• ipa (for IPA, International Phonetic Alphabet, support in transliteration)
• ml (to support ULMFiT models for classification)
• thai2fit (for Thai word vector)
• thai2rom (for machine-learnt romanization)
• wordnet (for Thai WordNet API)

For dependency details, look at the extras variable in setup.py.

• Some additional data, like word lists and language models, may be automatically downloaded during runtime.
• PyThaiNLP caches these data under the directory ~/pythainlp-data by default.
• The data directory can be changed by specifying the environment variable PYTHAINLP_DATA_DIR.
• See the data catalog (db.json) at https://github.com/PyThaiNLP/pythainlp-corpus

Some of PyThaiNLP functionalities can be used via command line with the thainlp command.

For example, to display a catalog of datasets:

thainlp data catalog

To show how to use:

thainlp help

• Please fork and create a pull request :)
• For style guides and other information, including references to algorithms we use, please refer to our contributing page.

You can read INTHEWILD.md.

If you use PyThaiNLP in your project or publication, please cite the library as follows:

Wannaphong Phatthiyaphaibun, Korakot Chaovavanich, Charin Polpanumas, Arthit Suriyawongkul, Lalita Lowphansirikul, & Pattarawat Chormai. (2016, Jun 27). PyThaiNLP: Thai Natural Language Processing in Python. Zenodo. http://doi.org/10.5281/zenodo.3519354

or by BibTeX entry:

@misc{pythainlp,
    title = "{P}y{T}hai{NLP}: {T}hai Natural Language Processing in {P}ython",
    author = "Phatthiyaphaibun, Wannaphong  and
      Chaovavanich, Korakot  and
      Polpanumas, Charin  and
      Suriyawongkul, Arthit  and
      Lowphansirikul, Lalita  and
      Chormai, Pattarawat",
    month = jun,
    year = "2016",
    doi = {10.5281/zenodo.3519354},
    publisher = {Zenodo},
    url = {http://doi.org/10.5281/zenodo.3519354}
}

Our NLP-OSS 2023 paper:

Wannaphong Phatthiyaphaibun, Korakot Chaovavanich, Charin Polpanumas, Arthit Suriyawongkul, Lalita Lowphansirikul, Pattarawat Chormai, Peerat Limkonchotiwat, Thanathip Suntorntip, and Can Udomcharoenchaikit. 2023. PyThaiNLP: Thai Natural Language Processing in Python. In Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023), pages 25–36, Singapore, Singapore. Empirical Methods in Natural Language Processing.

and its BibTeX entry:

@inproceedings{phatthiyaphaibun-etal-2023-pythainlp,
    title = "{P}y{T}hai{NLP}: {T}hai Natural Language Processing in {P}ython",
    author = "Phatthiyaphaibun, Wannaphong  and
      Chaovavanich, Korakot  and
      Polpanumas, Charin  and
      Suriyawongkul, Arthit  and
      Lowphansirikul, Lalita  and
      Chormai, Pattarawat  and
      Limkonchotiwat, Peerat  and
      Suntorntip, Thanathip  and
      Udomcharoenchaikit, Can",
    editor = "Tan, Liling  and
      Milajevs, Dmitrijs  and
      Chauhan, Geeticka  and
      Gwinnup, Jeremy  and
      Rippeth, Elijah",
    booktitle = "Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)",
    month = dec,
    year = "2023",
    address = "Singapore, Singapore",
    publisher = "Empirical Methods in Natural Language Processing",
    url = "https://aclanthology.org/2023.nlposs-1.4",
    pages = "25--36",
    abstract = "We present PyThaiNLP, a free and open-source natural language processing (NLP) library for Thai language implemented in Python. It provides a wide range of software, models, and datasets for Thai language. We first provide a brief historical context of tools for Thai language prior to the development of PyThaiNLP. We then outline the functionalities it provided as well as datasets and pre-trained language models. We later summarize its development milestones and discuss our experience during its development. We conclude by demonstrating how industrial and research communities utilize PyThaiNLP in their work. The library is freely available at https://github.com/pythainlp/pythainlp.",
}

Logo	Description
	Since 2019, our contributors Korakot Chaovavanich and Lalita Lowphansirikul have been supported by VISTEC-depa Thailand Artificial Intelligence Research Institute.
	We get support of free Mac Mini M1 from MacStadium for running CI builds.