pythainlp.tokenize

The pythainlp.tokenize contains multiple functions for tokenizing a chunk of Thai text into desirable units.

Modules

pythainlp.tokenize.clause_tokenize(doc: List[str]) → List[List[str]][source]

Clause tokenizer. (or Clause segmentation)

Tokenizes running word list into list of clauses (list of strings). split by CRF trained on LST20 Corpus.

It is free for non-commercial uses and research only. You can read at Facebook.

Parameters: doc (str) – word list to be clause
Returns: list of claues
Return type: list[list[str]]
Example

Clause tokenizer:

from pythainlp.tokenize import clause_tokenize

clause_tokenize(["ฉัน","นอน","และ","คุณ","เล่น","มือถือ","ส่วน","น้อง","เขียน","โปรแกรม"])
# [['ฉัน', 'นอน'],
# ['และ', 'คุณ', 'เล่น', 'มือถือ'],
# ['ส่วน', 'น้อง', 'เขียน', 'โปรแกรม']]

pythainlp.tokenize.sent_tokenize(text: str, engine: str = 'crfcut', keep_whitespace: bool = True) → List[str][source]

Sentence tokenizer.

Tokenizes running text into “sentences”

Parameters

text (str) – the text to be tokenized
engine (str) – choose among ‘crfcut’, ‘whitespace’, ‘whitespace+newline’

Returns

list of splited sentences

Return type

list[str]

Options for engine

crfcut - (default) split by CRF trained on TED dataset
whitespace+newline - split by whitespaces and newline.
whitespace - split by whitespaces. Specifiaclly, with regex pattern r" +"
tltk - split by TLTK.,
thaisum - The implementation of sentence segmentator from Nakhun Chumpolsathien, 2020

Example

Split the text based on whitespace:

from pythainlp.tokenize import sent_tokenize

sentence_1 = "ฉันไปประชุมเมื่อวันที่ 11 มีนาคม"
sentence_2 = "ข้าราชการได้รับการหมุนเวียนเป็นระยะ \
และได้รับมอบหมายให้ประจำในระดับภูมิภาค"

sent_tokenize(sentence_1, engine="whitespace")
# output: ['ฉันไปประชุมเมื่อวันที่', '11', 'มีนาคม']

sent_tokenize(sentence_2, engine="whitespace")
# output: ['ข้าราชการได้รับการหมุนเวียนเป็นระยะ',
#   '\nและได้รับมอบหมายให้ประจำในระดับภูมิภาค']

Split the text based on whitespace and newline:

sentence_1 = "ฉันไปประชุมเมื่อวันที่ 11 มีนาคม"
sentence_2 = "ข้าราชการได้รับการหมุนเวียนเป็นระยะ \
และได้รับมอบหมายให้ประจำในระดับภูมิภาค"

sent_tokenize(sentence_1, engine="whitespace+newline")
# output: ['ฉันไปประชุมเมื่อวันที่', '11', 'มีนาคม']
sent_tokenize(sentence_2, engine="whitespace+newline")
# output: ['ข้าราชการได้รับการหมุนเวียนเป็นระยะ',
'\nและได้รับมอบหมายให้ประจำในระดับภูมิภาค']

Split the text using CRF trained on TED dataset:

sentence_1 = "ฉันไปประชุมเมื่อวันที่ 11 มีนาคม"
sentence_2 = "ข้าราชการได้รับการหมุนเวียนเป็นระยะ \
และเขาได้รับมอบหมายให้ประจำในระดับภูมิภาค"

sent_tokenize(sentence_1, engine="crfcut")
# output: ['ฉันไปประชุมเมื่อวันที่ 11 มีนาคม']

sent_tokenize(sentence_2, engine="crfcut")
# output: ['ข้าราชการได้รับการหมุนเวียนเป็นระยะ ',
'และเขาได้รับมอบหมายให้ประจำในระดับภูมิภาค']

pythainlp.tokenize.subword_tokenize(text: str, engine: str = 'tcc', keep_whitespace: bool = True) → List[str][source]

Subword tokenizer. Can be smaller than syllable.

Tokenizes text into inseparable units of Thai contiguous characters namely Thai Character Clusters (TCCs) TCCs are the units based on Thai spelling feature that could not be separated any character further such as ‘ก็’, ‘จะ’, ‘ไม่’, and ‘ฝา’. If the following units are separated, they could not be spelled out. This function apply the TCC rules to tokenizes the text into the smallest units.

For example, the word ‘ขนมชั้น’ would be tokenized into ‘ข’, ‘น’, ‘ม’, and ‘ชั้น’.

Parameters

text (str) – text to be tokenized
engine (str) – the name subword tokenizer

Returns

list of subwords

Return type

list[str]

Options for engine

tcc (default) - Thai Character Cluster (Theeramunkong et al. 2000)
etcc - Enhanced Thai Character Cluster (Inrut et al. 2001)
wangchanberta - SentencePiece from wangchanberta model.
dict - newmm word tokenizer with a syllable dictionary
ssg - CRF syllable segmenter for Thai
tltk - syllable tokenizer from tltk

Example

Tokenize text into subword based on tcc:

from pythainlp.tokenize import subword_tokenize

text_1 = "ยุคเริ่มแรกของ ราชวงศ์หมิง"
text_2 = "ความแปลกแยกและพัฒนาการ"

subword_tokenize(text_1, engine='tcc')
# output: ['ยุ', 'ค', 'เริ่ม', 'แร', 'ก',
#   'ข', 'อ', 'ง', ' ', 'รา', 'ช', 'ว', 'ง',
#   'ศ', '์', 'ห', 'มิ', 'ง']

subword_tokenize(text_2, engine='tcc')
# output: ['ค', 'วา', 'ม', 'แป', 'ล', 'ก', 'แย', 'ก',
'และ', 'พัฒ','นา', 'กา', 'ร']

Tokenize text into subword based on etcc:

text_1 = "ยุคเริ่มแรกของ ราชวงศ์หมิง"
text_2 = "ความแปลกแยกและพัฒนาการ"

subword_tokenize(text_1, engine='etcc')
# output: ['ยุคเริ่มแรกของ ราชวงศ์หมิง']

subword_tokenize(text_2, engine='etcc')
# output: ['ความแปลกแยกและ', 'พัฒ', 'นาการ']

Tokenize text into subword based on wangchanberta:

text_1 = "ยุคเริ่มแรกของ ราชวงศ์หมิง"
text_2 = "ความแปลกแยกและพัฒนาการ"

subword_tokenize(text_1, engine='wangchanberta')
# output: ['▁', 'ยุค', 'เริ่มแรก', 'ของ', '▁', 'ราชวงศ์', 'หมิง']

subword_tokenize(text_2, engine='wangchanberta')
# output: ['▁ความ', 'แปลก', 'แยก', 'และ', 'พัฒนาการ']

pythainlp.tokenize.word_tokenize(text: str, custom_dict: Optional[Trie] = None, engine: str = 'newmm', keep_whitespace: bool = True) → List[str][source]

Word tokenizer.

Tokenizes running text into words (list of strings).

Parameters

text (str) – text to be tokenized
engine (str) – name of the tokenizer to be used
custom_dict (pythainlp.util.Trie) – dictionary trie
keep_whitespace (bool) – True to keep whitespaces, a common mark for end of phrase in Thai. Otherwise, whitespaces are omitted.

Returns

list of words

Return type

List[str]

Options for engine

newmm (default) - dictionary-based, Maximum Matching + Thai Character Cluster
newmm-safe - newmm, with a mechanism to help avoid long processing time for text with continuous ambiguous breaking points
mm or multi_cut - dictionary-based, Maximum Matching.
nlpo3 - Python binding for nlpO3. It is newmm engine in Rust.
longest - dictionary-based, Longest Matching
icu - wrapper for ICU (International Components for Unicode, using PyICU), dictionary-based
attacut - wrapper for AttaCut., learning-based approach
deepcut - wrapper for DeepCut, learning-based approach
nercut - Dictionary-based maximal matching word segmentation, constrained with Thai Character Cluster (TCC) boundaries, and combining tokens that are parts of the same named-entity.
sefr_cut - wrapper for SEFR CUT.,
tltk - wrapper for TLTK.,
oskut - wrapper for OSKut.,

Note

The parameter custom_dict can be provided as an argument only for newmm, longest, and deepcut engine.

Example

Tokenize text with different tokenizer:

from pythainlp.tokenize import word_tokenize

text = "โอเคบ่พวกเรารักภาษาบ้านเกิด"

word_tokenize(text, engine="newmm")
# output: ['โอเค', 'บ่', 'พวกเรา', 'รัก', 'ภาษา', 'บ้านเกิด']

word_tokenize(text, engine='attacut')
# output: ['โอเค', 'บ่', 'พวกเรา', 'รัก', 'ภาษา', 'บ้านเกิด']

Tokenize text by omiting whitespaces:

text = "วรรณกรรม ภาพวาด และการแสดงงิ้ว "

word_tokenize(text, engine="newmm")
# output:
# ['วรรณกรรม', ' ', 'ภาพวาด', ' ', 'และ', 'การแสดง', 'งิ้ว', ' ']

word_tokenize(text, engine="newmm", keep_whitespace=False)
# output: ['วรรณกรรม', 'ภาพวาด', 'และ', 'การแสดง', 'งิ้ว']

Tokenize with default and custom dictionary:

from pythainlp.corpus.common import thai_words
from pythainlp.tokenize import dict_trie

text = 'ชินโซ อาเบะ เกิด 21 กันยายน'

word_tokenize(text, engine="newmm")
# output:
# ['ชิน', 'โซ', ' ', 'อา', 'เบะ', ' ',
#  'เกิด', ' ', '21', ' ', 'กันยายน']

custom_dict_japanese_name = set(thai_words()
custom_dict_japanese_name.add('ชินโซ')
custom_dict_japanese_name.add('อาเบะ')

trie = dict_trie(dict_source=custom_dict_japanese_name)

word_tokenize(text, engine="newmm", custom_dict=trie))
# output:
# ['ชินโซ', ' ', 'อาเบะ',
#   ' ', 'เกิด', ' ', '21', ' ', 'กันยายน']

pythainlp.tokenize.word_detokenize(segments: Union[List[List[str]], List[str]], output: str = 'str') → Union[str, List[str]][source]

Word detokenizer.

This function will detokenize the list word in each sentence to text.

Parameters

segments (str) – List sentences with list words.
output (str) – the output type (str or list)

Returns

the thai text

Return type

Union[str,List[str]]

class pythainlp.tokenize.Tokenizer(custom_dict: Optional[Union[Trie, Iterable[str], str]] = None, engine: str = 'newmm', keep_whitespace: bool = True)[source]

Tokenizer class, for a custom tokenizer.

This class allows users to pre-define custom dictionary along with tokenizer and encapsulate them into one single object. It is an wrapper for both two functions including pythainlp.tokenize.word_tokenize(), and pythainlp.util.dict_trie()

Example

Tokenizer object instantiated with pythainlp.util.Trie:

from pythainlp.tokenize import Tokenizer
from pythainlp.corpus.common import thai_words
from pythainlp.util import dict_trie

custom_words_list = set(thai_words())
custom_words_list.add('อะเฟเซีย')
custom_words_list.add('Aphasia')
trie = dict_trie(dict_source=custom_words_list)

text = "อะเฟเซีย (Aphasia*) เป็นอาการผิดปกติของการพูด"
_tokenizer = Tokenizer(custom_dict=trie, engine='newmm')
_tokenizer.word_tokenize(text)
# output: ['อะเฟเซีย', ' ', '(', 'Aphasia', ')', ' ', 'เป็น', 'อาการ',
'ผิดปกติ', 'ของ', 'การ', 'พูด']

Tokenizer object instantiated with a list of words:

text = "อะเฟเซีย (Aphasia) เป็นอาการผิดปกติของการพูด"
_tokenizer = Tokenizer(custom_dict=list(thai_words()), engine='newmm')
_tokenizer.word_tokenize(text)
# output:
# ['อะ', 'เฟเซีย', ' ', '(', 'Aphasia', ')', ' ', 'เป็น', 'อาการ',
#   'ผิดปกติ', 'ของ', 'การ', 'พูด']

Tokenizer object instantiated with a file path containing list of word separated with newline and explicitly set a new tokenizer after initiation:

PATH_TO_CUSTOM_DICTIONARY = './custom_dictionary.txtt'

# write a file
with open(PATH_TO_CUSTOM_DICTIONARY, 'w', encoding='utf-8') as f:
    f.write('อะเฟเซีย\nAphasia\nผิด\nปกติ')

text = "อะเฟเซีย (Aphasia) เป็นอาการผิดปกติของการพูด"

# initate an object from file with `attacut` as tokenizer
_tokenizer = Tokenizer(custom_dict=PATH_TO_CUSTOM_DICTIONARY, \
    engine='attacut')

_tokenizer.word_tokenize(text)
# output:
# ['อะเฟเซีย', ' ', '(', 'Aphasia', ')', ' ', 'เป็น', 'อาการ', 'ผิด',
#   'ปกติ', 'ของ', 'การ', 'พูด']

# change tokenizer to `newmm`
_tokenizer.set_tokenizer_engine(engine='newmm')
_tokenizer.word_tokenize(text)
# output:
# ['อะเฟเซีย', ' ', '(', 'Aphasia', ')', ' ', 'เป็นอาการ', 'ผิด',
#   'ปกติ', 'ของการพูด']

__init__(custom_dict: Optional[Union[Trie, Iterable[str], str]] = None, engine: str = 'newmm', keep_whitespace: bool = True)[source]

Initialize tokenizer object.

Parameters

custom_dict (str) – a file path, a list of vocaburaies* to be used to create a trie, or an instantiated pythainlp.util.Trie object.
engine (str) – choose between different options of engine to token (i.e. newmm, mm, longest, deepcut)
keep_whitespace (bool) – True to keep whitespaces, a common mark for end of phrase in Thai

word_tokenize(text: str) → List[str][source]

Main tokenization function.

Parameters: text (str) – text to be tokenized
Returns: list of words, tokenized from the text
Return type: list[str]

set_tokenize_engine(engine: str) → None[source]

Set the tokenizer’s engine.

Parameters: engine (str) – choose between different options of engine to token (i.e. newmm, mm, longest, deepcut)

Tokenization Engines

Sentence level

crfcut

CRFCut - Thai sentence segmenter.

Thai sentence segmentation using conditional random field, default model trained on TED dataset

Performance: - ORCHID - space-correct accuracy 87% vs 95% state-of-the-art

(Zhou et al, 2016; https://www.aclweb.org/anthology/C16-1031.pdf)

TED dataset - space-correct accuracy 82%

See development notebooks at https://github.com/vistec-AI/ted_crawler; POS features are not used due to unreliable POS tagging available

pythainlp.tokenize.crfcut.extract_features(doc: List[str], window: int = 2, max_n_gram: int = 3) → List[List[str]][source]

Extract features for CRF by sliding max_n_gram of tokens for +/- window from the current token

Parameters

doc (List[str]) – tokens from which features are to be extracted from
window (int) – size of window before and after the current token
max_n_gram (int) – create n_grams from 1-gram to max_n_gram-gram within the window

Returns

list of lists of features to be fed to CRF

pythainlp.tokenize.crfcut.segment(text: str) → List[str][source]

CRF-based sentence segmentation.

Parameters: text (str) – text to be tokenized to sentences
Returns: list of words, tokenized from the text

pythainlp.tokenize.crfcut.extract_features(doc: List[str], window: int = 2, max_n_gram: int = 3) → List[List[str]][source]

Extract features for CRF by sliding max_n_gram of tokens for +/- window from the current token

Parameters

doc (List[str]) – tokens from which features are to be extracted from
window (int) – size of window before and after the current token
max_n_gram (int) – create n_grams from 1-gram to max_n_gram-gram within the window

Returns

list of lists of features to be fed to CRF

pythainlp.tokenize.crfcut.segment(text: str) → List[str][source]

CRF-based sentence segmentation.

Parameters: text (str) – text to be tokenized to sentences
Returns: list of words, tokenized from the text

thaisumcut

The implementation of sentence segmentator from Nakhun Chumpolsathien, 2020 original code from: https://github.com/nakhunchumpolsathien/ThaiSum

Cite:

@mastersthesis{chumpolsathien_2020,: title={Using Knowledge Distillation from Keyword Extraction to Improve the Informativeness of Neural Cross-lingual Summarization}, author={Chumpolsathien, Nakhun}, year={2020}, school={Beijing Institute of Technology}

ThaiSum License

Copyright [2020 [Nakhun Chumpolsathien]

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

pythainlp.tokenize.thaisumcut.list_to_string(list: List[str]) → str[source]

pythainlp.tokenize.thaisumcut.middle_cut(sentences: List[str]) → List[str][source]

class pythainlp.tokenize.thaisumcut.ThaiSentenceSegmentor[source]

split_into_sentences(text: str, isMiddleCut: bool = False) → List[str][source]

pythainlp.tokenize.thaisumcut.list_to_string(list: List[str]) → str[source]

pythainlp.tokenize.thaisumcut.middle_cut(sentences: List[str]) → List[str][source]

class pythainlp.tokenize.thaisumcut.ThaiSentenceSegmentor[source]

split_into_sentences(text: str, isMiddleCut: bool = False) → List[str][source]

Word level

attacut

Wrapper for AttaCut - Fast and Reasonably Accurate Word Tokenizer for Thai

See Also

GitHub repository

class pythainlp.tokenize.attacut.AttacutTokenizer(model='attacut-sc')[source]

__init__(model='attacut-sc')[source]

tokenize(text: str) → List[str][source]

pythainlp.tokenize.attacut.segment(text: str, model: str = 'attacut-sc') → List[str][source]

Wrapper for AttaCut - Fast and Reasonably Accurate Word Tokenizer for Thai :param str text: text to be tokenized to words :param str model: word tokenizer model to be tokenized to words :return: list of words, tokenized from the text :rtype: list[str] Options for model

attacut-sc (default) using both syllable and character features

attacut-c using only character feature

class pythainlp.tokenize.attacut.AttacutTokenizer(model='attacut-sc')[source]

__init__(model='attacut-sc')[source]

tokenize(text: str) → List[str][source]

deepcut

Wrapper for deepcut Thai word segmentation. deepcut is a Thai word segmentation library using 1D Convolution Neural Network.

User need to install deepcut (and its dependency: tensorflow) by themselves.

See Also

GitHub repository

pythainlp.tokenize.deepcut.segment(text: str, custom_dict: Optional[Union[Trie, List[str], str]] = None) → List[str][source]

multi_cut

Multi cut – Thai word segmentation with maximum matching. Original code from Korakot Chaovavanich.

See Also

class pythainlp.tokenize.multi_cut.LatticeString(value, multi=None, in_dict=True)[source]

String that keeps possible tokenizations

__init__(value, multi=None, in_dict=True)[source]

pythainlp.tokenize.multi_cut.mmcut(text: str) → List[str][source]

pythainlp.tokenize.multi_cut.segment(text: str, custom_dict: ~pythainlp.util.trie.Trie = <pythainlp.util.trie.Trie object>) → List[str][source]

Dictionary-based maximum matching word segmentation.

Parameters

text (str) – text to be tokenized
custom_dict (Trie, optional) – tokenization dictionary, defaults to DEFAULT_WORD_DICT_TRIE

Returns

list of segmented tokens

Return type

List[str]

pythainlp.tokenize.multi_cut.find_all_segment(text: str, custom_dict: ~pythainlp.util.trie.Trie = <pythainlp.util.trie.Trie object>) → List[str][source]

Get all possible segment variations.

Parameters

text (str) – input string to be tokenized
custom_dict (Trie, optional) – tokenization dictionary, defaults to DEFAULT_WORD_DICT_TRIE

Returns

list of segment variations

Return type

List[str]

pythainlp.tokenize.multi_cut.segment(text: str, custom_dict: ~pythainlp.util.trie.Trie = <pythainlp.util.trie.Trie object>) → List[str][source]

Dictionary-based maximum matching word segmentation.

Parameters

text (str) – text to be tokenized
custom_dict (Trie, optional) – tokenization dictionary, defaults to DEFAULT_WORD_DICT_TRIE

Returns

list of segmented tokens

Return type

List[str]

pythainlp.tokenize.multi_cut.find_all_segment(text: str, custom_dict: ~pythainlp.util.trie.Trie = <pythainlp.util.trie.Trie object>) → List[str][source]

Get all possible segment variations.

Parameters

text (str) – input string to be tokenized
custom_dict (Trie, optional) – tokenization dictionary, defaults to DEFAULT_WORD_DICT_TRIE

Returns

list of segment variations

Return type

List[str]

nlpo3

pythainlp.tokenize.nlpo3.load_dict(file_path: str, dict_name: str) → bool[source]

Load a dictionary file into an in-memory dictionary collection.

The loaded dictionary will be accessible throught the assigned dict_name. * This function does not override an existing dict name. *

Parameters

file_path (str) – Path to a dictionary file
dict_name (str) – A unique dictionary name, use for reference.

:return bool

See Also

https://github.com/PyThaiNLP/nlpo3

pythainlp.tokenize.nlpo3.segment(text: str, custom_dict: str = '_67a47bf9', safe_mode: bool = False, parallel_mode: bool = False) → List[str][source]

Break text into tokens.

Python binding for nlpO3. It is newmm engine in Rust.

Parameters

text (str) – text to be tokenized
custom_dict (str) – dictionary name, as assigned with load_dict(), defaults to pythainlp/corpus/common/words_th.txt
safe_mode (bool) – reduce chance for long processing time in long text with many ambiguous breaking points, defaults to False
parallel_mode (bool) – Use multithread mode, defaults to False

Returns

list of tokens

Return type

List[str]

See Also

https://github.com/PyThaiNLP/nlpo3

pythainlp.tokenize.nlpo3.load_dict(file_path: str, dict_name: str) → bool[source]

Load a dictionary file into an in-memory dictionary collection.

The loaded dictionary will be accessible throught the assigned dict_name. * This function does not override an existing dict name. *

Parameters

file_path (str) – Path to a dictionary file
dict_name (str) – A unique dictionary name, use for reference.

:return bool

See Also

https://github.com/PyThaiNLP/nlpo3

pythainlp.tokenize.nlpo3.segment(text: str, custom_dict: str = '_67a47bf9', safe_mode: bool = False, parallel_mode: bool = False) → List[str][source]

Break text into tokens.

Python binding for nlpO3. It is newmm engine in Rust.

Parameters

text (str) – text to be tokenized
custom_dict (str) – dictionary name, as assigned with load_dict(), defaults to pythainlp/corpus/common/words_th.txt
safe_mode (bool) – reduce chance for long processing time in long text with many ambiguous breaking points, defaults to False
parallel_mode (bool) – Use multithread mode, defaults to False

Returns

list of tokens

Return type

List[str]

See Also

https://github.com/PyThaiNLP/nlpo3

longest

Dictionary-based longest-matching Thai word segmentation. Implementation based on the code from Patorn Utenpattanun.

See Also

GitHub Repository

class pythainlp.tokenize.longest.LongestMatchTokenizer(trie: Trie)[source]

__init__(trie: Trie)[source]

tokenize(text: str) → List[str][source]

pythainlp.tokenize.longest.segment(text: str, custom_dict: ~pythainlp.util.trie.Trie = <pythainlp.util.trie.Trie object>) → List[str][source]

Dictionary-based longest matching word segmentation.

Parameters

text (str) – text to be tokenized to words
custom_dict (pythainlp.util.Trie) – dictionary for tokenization

Returns

list of words, tokenized from the text

pythainlp.tokenize.longest.segment(text: str, custom_dict: ~pythainlp.util.trie.Trie = <pythainlp.util.trie.Trie object>) → List[str][source]

Dictionary-based longest matching word segmentation.

Parameters

text (str) – text to be tokenized to words
custom_dict (pythainlp.util.Trie) – dictionary for tokenization

Returns

list of words, tokenized from the text

pyicu

Wrapper for PyICU word segmentation. This wrapper module uses icu.BreakIterator with Thai as icu.Local to locate boundaries between words from the text.

See Also

GitHub repository

pythainlp.tokenize.pyicu.segment(text: str) → List[str][source]

Parameters: text (str) – text to be tokenized to words
Returns: list of words, tokenized from the text

nercut

nercut 0.2

Dictionary-based maximal matching word segmentation, constrained with Thai Character Cluster (TCC) boundaries, and combining tokens that are parts of the same named-entity.

Code by Wannaphong Phatthiyaphaibun

pythainlp.tokenize.nercut.segment(text: str, taglist: ~typing.Iterable[str] = ['ORGANIZATION', 'PERSON', 'PHONE', 'EMAIL', 'DATE', 'TIME'], tagger=<pythainlp.tag.named_entity.NER object>) → List[str][source]

Dictionary-based maximal matching word segmentation, constrained with Thai Character Cluster (TCC) boundaries, and combining tokens that are parts of the same named-entity.

Parameters: text (str) – text to be tokenized to words
Parm list taglist: a list of named-entity tags to be used
Parm class tagger: ner tagger engine
Returns: list of words, tokenized from the text

pythainlp.tokenize.nercut.segment(text: str, taglist: ~typing.Iterable[str] = ['ORGANIZATION', 'PERSON', 'PHONE', 'EMAIL', 'DATE', 'TIME'], tagger=<pythainlp.tag.named_entity.NER object>) → List[str][source]

Dictionary-based maximal matching word segmentation, constrained with Thai Character Cluster (TCC) boundaries, and combining tokens that are parts of the same named-entity.

Parameters: text (str) – text to be tokenized to words
Parm list taglist: a list of named-entity tags to be used
Parm class tagger: ner tagger engine
Returns: list of words, tokenized from the text

sefr_cut

Wrapper for SEFR CUT Thai word segmentation. SEFR CUT is a Thai Word Segmentation Models using Stacked Ensemble.

See Also

GitHub repository

pythainlp.tokenize.sefr_cut.segment(text: str, engine: str = 'ws1000') → List[str][source]

oskut

Wrapper OSKut (Out-of-domain StacKed cut for Word Segmentation). Handling Cross- and Out-of-Domain Samples in Thai Word Segmentation Stacked Ensemble Framework and DeepCut as Baseline model (ACL 2021 Findings)

See Also

GitHub repository

pythainlp.tokenize.oskut.segment(text: str, engine: str = 'ws') → List[str][source]

newmm

The default word tokenization engine.

Dictionary-based maximal matching word segmentation, constrained with Thai Character Cluster (TCC) boundaries.

The code is based on the notebooks created by Korakot Chaovavanich, with heuristic graph size limit added to avoid exponential wait time.

See Also

pythainlp.tokenize.newmm.segment(text: str, custom_dict: ~pythainlp.util.trie.Trie = <pythainlp.util.trie.Trie object>, safe_mode: bool = False) → List[str][source]

Maximal-matching word segmentation, Thai Character Cluster constrained.

A dictionary-based word segmentation using maximal matching algorithm, constrained to Thai Character Cluster boundaries.

A custom dictionary can be supplied.

Parameters

text (str) – text to be tokenized
custom_dict (Trie, optional) – tokenization dictionary, defaults to DEFAULT_WORD_DICT_TRIE
safe_mode (bool, optional) – reduce chance for long processing time in long text with many ambiguous breaking points, defaults to False

Returns

list of tokens

Return type

List[str]

pythainlp.tokenize.newmm.segment(text: str, custom_dict: ~pythainlp.util.trie.Trie = <pythainlp.util.trie.Trie object>, safe_mode: bool = False) → List[str][source]

Maximal-matching word segmentation, Thai Character Cluster constrained.

A dictionary-based word segmentation using maximal matching algorithm, constrained to Thai Character Cluster boundaries.

A custom dictionary can be supplied.

Parameters

text (str) – text to be tokenized
custom_dict (Trie, optional) – tokenization dictionary, defaults to DEFAULT_WORD_DICT_TRIE
safe_mode (bool, optional) – reduce chance for long processing time in long text with many ambiguous breaking points, defaults to False

Returns

list of tokens

Return type

List[str]

Subword level

tcc

The implementation of tokenizer accorinding to Thai Character Clusters (TCCs) rules purposed by Theeramunkong et al. 2000.

Credits:

TCC: Jakkrit TeCho
Grammar: Wittawat Jitkrittum (link to the source file)
Python code: Korakot Chaovavanich

pythainlp.tokenize.tcc.tcc(text: str) → str[source]

TCC generator, generates Thai Character Clusters

Parameters: text (str) – text to be tokenized to character clusters
Returns: subwords (character clusters)
Return type: Iterator[str]

pythainlp.tokenize.tcc.tcc_pos(text: str) → Set[int][source]

TCC positions

Parameters: text (str) – text to be tokenized to character clusters
Returns: list of the end position of subwords
Return type: set[int]

pythainlp.tokenize.tcc.segment(text: str) → List[str][source]

Subword segmentation

Parameters: text (str) – text to be tokenized to character clusters
Returns: list of subwords (character clusters), tokenized from the text
Return type: list[str]

pythainlp.tokenize.tcc.segment(text: str) → List[str][source]

Subword segmentation

Parameters: text (str) – text to be tokenized to character clusters
Returns: list of subwords (character clusters), tokenized from the text
Return type: list[str]

pythainlp.tokenize.tcc.tcc(text: str) → str[source]

TCC generator, generates Thai Character Clusters

Parameters: text (str) – text to be tokenized to character clusters
Returns: subwords (character clusters)
Return type: Iterator[str]

pythainlp.tokenize.tcc.tcc_pos(text: str) → Set[int][source]

TCC positions

Parameters: text (str) – text to be tokenized to character clusters
Returns: list of the end position of subwords
Return type: set[int]

etcc

Segmenting text to Enhanced Thai Character Cluster (ETCC) Python implementation by Wannaphong Phatthiyaphaibun

This implementation relies on a dictionary of ETCC created from etcc.txt in pythainlp/corpus.

Notebook: https://colab.research.google.com/drive/1UTQgxxMRxOr9Jp1B1jcq1frBNvorhtBQ

See Also

Inrut, Jeeragone, Patiroop Yuanghirun, Sarayut Paludkong, Supot Nitsuwat, and Para Limmaneepraserth. “Thai word segmentation using combination of forward and backward longest matching techniques.” In International Symposium on Communications and Information Technology (ISCIT), pp. 37-40. 2001.

pythainlp.tokenize.etcc.segment(text: str) → List[str][source]

Segmenting text into ETCCs.

Enhanced Thai Character Cluster (ETCC) is a kind of subword unit. The concept was presented in Inrut, Jeeragone, Patiroop Yuanghirun, Sarayut Paludkong, Supot Nitsuwat, and Para Limmaneepraserth. “Thai word segmentation using combination of forward and backward longest matching techniques.” In International Symposium on Communications and Information Technology (ISCIT), pp. 37-40. 2001.

Parameters: text (str) – text to be tokenized to character clusters
Returns: list of clusters, tokenized from the text
Returns: list[str]

pythainlp.tokenize.etcc.segment(text: str) → List[str][source]

Segmenting text into ETCCs.

Enhanced Thai Character Cluster (ETCC) is a kind of subword unit. The concept was presented in Inrut, Jeeragone, Patiroop Yuanghirun, Sarayut Paludkong, Supot Nitsuwat, and Para Limmaneepraserth. “Thai word segmentation using combination of forward and backward longest matching techniques.” In International Symposium on Communications and Information Technology (ISCIT), pp. 37-40. 2001.

Parameters: text (str) – text to be tokenized to character clusters
Returns: list of clusters, tokenized from the text
Returns: list[str]