| .. currentmodule:: pythainlp.tokenize | |
| .. _tokenize-doc: | |
| pythainlp.tokenize | |
| ================== | |
| The :mod:`pythainlp.tokenize` module contains a comprehensive set of functions and classes for tokenizing Thai text into various units, such as sentences, words, subwords, and more. This module is a fundamental component of the PyThaiNLP library, providing tools for natural language processing in the Thai language. | |
| Modules | |
| ------- | |
| .. autofunction:: sent_tokenize | |
| :noindex: | |
| Splits Thai text into sentences. This function identifies sentence boundaries, which is essential for text segmentation and analysis. | |
| .. autofunction:: paragraph_tokenize | |
| :noindex: | |
| Segments text into paragraphs, which can be valuable for document-level analysis or summarization. | |
| .. autofunction:: subword_tokenize | |
| :noindex: | |
| Tokenizes text into subwords, which can be helpful for various NLP tasks, including subword embeddings. | |
| .. autofunction:: syllable_tokenize | |
| :noindex: | |
| Divides text into syllables, allowing you to work with individual Thai language phonetic units. | |
| .. autofunction:: word_tokenize | |
| :noindex: | |
| Splits text into words. This function is a fundamental tool for Thai language text analysis. | |
| .. autofunction:: word_detokenize | |
| :noindex: | |
| Reverses the tokenization process, reconstructing text from tokenized units. Useful for text generation tasks. | |
| .. autoclass:: Tokenizer | |
| :members: | |
| The `Tokenizer` class is a versatile tool for customizing tokenization processes and managing tokenization models. It provides various methods and attributes to fine-tune tokenization according to your specific needs. | |
| Tokenization Engines | |
| -------------------- | |
| This module offers multiple tokenization engines designed for different levels of text analysis. | |
| Sentence level | |
| -------------- | |
| **crfcut** | |
| .. automodule:: pythainlp.tokenize.crfcut | |
| :members: | |
| A tokenizer that operates at the sentence level using Conditional Random Fields (CRF). It is suitable for segmenting text into sentences accurately. | |
| **thaisumcut** | |
| .. automodule:: pythainlp.tokenize.thaisumcut | |
| :members: | |
| A sentence tokenizer based on a maximum entropy model. It's a great choice for sentence boundary detection in Thai text. | |
| Word level | |
| ---------- | |
| **attacut** | |
| .. automodule:: pythainlp.tokenize.attacut | |
| :members: | |
| A tokenizer designed for word-level segmentation. It provides accurate word boundary detection in Thai text. | |
| **deepcut** | |
| .. automodule:: pythainlp.tokenize.deepcut | |
| :members: | |
| Utilizes deep learning techniques for word segmentation, achieving high accuracy and performance. | |
| **multi_cut** | |
| .. automodule:: pythainlp.tokenize.multi_cut | |
| :members: | |
| An ensemble tokenizer that combines multiple tokenization strategies for improved word segmentation. | |
| **nlpo3** | |
| .. automodule:: pythainlp.tokenize.nlpo3 | |
| :members: | |
| A word tokenizer based on the NLPO3 model. It offers advanced word boundary detection and is suitable for various NLP tasks. | |
| **longest** | |
| .. automodule:: pythainlp.tokenize.longest | |
| :members: | |
| A tokenizer that identifies word boundaries by selecting the longest possible words in a text. | |
| **pyicu** | |
| .. automodule:: pythainlp.tokenize.pyicu | |
| :members: | |
| An ICU-based word tokenizer offering robust support for Thai text segmentation. | |
| **nercut** | |
| .. automodule:: pythainlp.tokenize.nercut | |
| :members: | |
| A tokenizer optimized for Named Entity Recognition (NER) tasks, ensuring accurate tokenization for entity recognition. | |
| **sefr_cut** | |
| .. automodule:: pythainlp.tokenize.sefr_cut | |
| :members: | |
| An advanced word tokenizer for segmenting Thai text, with a focus on precision. | |
| **oskut** | |
| .. automodule:: pythainlp.tokenize.oskut | |
| :members: | |
| A tokenizer that uses a pre-trained model for word segmentation. It's a reliable choice for general-purpose text analysis. | |
| **newmm (Default)** | |
| .. automodule:: pythainlp.tokenize.newmm | |
| :members: | |
| The default word tokenization engine that provides a balance between accuracy and efficiency for most use cases. | |
| Subword level | |
| ------------- | |
| **tcc** | |
| .. automodule:: pythainlp.tokenize.tcc | |
| :members: | |
| Tokenizes text into Thai Character Clusters (TCCs), a subword level representation. | |
| **tcc+** | |
| .. automodule:: pythainlp.tokenize.tcc_p | |
| :members: | |
| A subword tokenizer that includes additional rules for more precise subword segmentation. | |
| **etcc** | |
| .. automodule:: pythainlp.tokenize.etcc | |
| :members: | |
| Enhanced Thai Character Clusters (eTCC) tokenizer for subword-level analysis. | |
| **han_solo** | |
| .. automodule:: pythainlp.tokenize.han_solo | |
| :members: | |
| A subword tokenizer specialized for Han characters and mixed scripts, suitable for various text processing scenarios. | |