pythainlp.tokenize¶

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

Modules¶

pythainlp.tokenize.sent_tokenize(text: str, engine: str = 'whitespace+newline') → List[str][source]¶

This function does not yet automatically recognize when a sentence actually ends. Rather it helps split text where white space and a new line is found.

Parameters

text (str) – the text to be tokenized
engine (str) – choose between ‘whitespace’ or ‘whitespace+newline’

Returns

list of splited sentences

Return type

list[str]

Options for engine

whitespace+newline (default) - split by whitespace token and newline.
whitespace - split by whitespace token. Specifiaclly, with regex pattern r" +"

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:

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

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

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

This function tokenizes running text into words.

Parameters

text (str) – text to be tokenized
engine (str) – name of the tokenizer to be used
custom_dict (pythainlp.tokenize.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 avoid long processing time for some long continuous text without spaces
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

Warning

the option for engine named ulmfit has been deprecated since PyThaiNLP version 2.1

Note

The parameter custom_dict can be provided as an argument only for newmm, longest, and attacut 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.syllable_tokenize(text: str, engine: str = 'default') → List[str][source]¶

This function is to tokenize text into syllable (Thai: พยางค์), a unit of pronunciation having one vowel sound. For example, the word ‘รถไฟ’ contains two syallbles including ‘รถ’, and ‘ไฟ’. Under the hood, this function uses pythainlp.tokenize.word_tokenize() with newmm as a tokenizer. The function tokenize the text with the dictionary of Thai words from pythainlp.corpus.common.thai_words() and then dictionary of Thai syllable from pythainlp.corpus.common.thai_syllables(). As a result, only syllables are obtained.

Parameters

text (str) – input string to be tokenized
engine (str) – name of the syllable tokenizer

Returns

list of syllables where whitespaces in the text are included

Return type

list[str]

Options for engine

default
ssg - CRF syllable segmenter for Thai.

Example:

from pythainlp.tokenize import syllable_tokenize

text = 'รถไฟสมัยใหม่จะใช้กำลังจากหัวรถจักรดีเซล หรือจากไฟฟ้า'
syllable_tokenize(text)
['รถ', 'ไฟ', 'สมัย', 'ใหม่', 'ใช้', 'กำ', 'ลัง', 'จาก', 'หัว',
'รถ', 'จักร', 'ดี', 'เซล', ' ', 'หรือ', 'จาก', 'ไฟ', 'ฟ้า']

pythainlp.tokenize.subword_tokenize(text: str, engine: str = 'tcc') → List[str][source]¶

This function 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)
ssg - CRF syllable segmenter for Thai.
etcc - Enhanced Thai Character Cluster (Inrut et al. 2001) [In development]

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 (Work In Progress):

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']

pythainlp.tokenize.dict_trie(dict_source: Union[str, Iterable[str], pythainlp.tokenize.trie.Trie]) → pythainlp.tokenize.trie.Trie[source]¶

Create a dictionary trie which will be used for word_tokenize() function.

Parameters: dict_source (str|Iterable[str]|pythainlp.tokenize.Trie) – a path to dictionary file or a list of words or a pythainlp.tokenize.Trie object
Returns: a trie object created from a dictionary input
Return type: pythainlp.tokenize.Trie

class pythainlp.tokenize.Tokenizer(custom_dict: Optional[Union[pythainlp.tokenize.trie.Trie, Iterable[str], str]] = None, engine: str = 'newmm')[source]¶

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.tokenize.dict_trie()

Example

Tokenizer object instantiated with pythainlp.tokenize.Trie:

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

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')
# 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']

set_tokenize_engine(engine: str) → None [source]¶: Set the tokenizer :param str engine: choose between different options of engine to token

(i.e. newmm, longest, attacut)

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

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

Tokenization Engines¶

newmm¶

Dictionary-based Thai Word Segmentation using maximal matching algorithm and Thai Character Cluster (TCC). The code is based on the notebooks created by Korakot Chaovavanich. :See Also:

https://colab.research.google.com/notebook#fileId=1V1Z657_5eSWPo8rLfVRwA0A5E4vkg7SI

https://colab.research.google.com/drive/14Ibg-ngZXj15RKwjNwoZlOT32fQBOrBx#scrollTo=MYZ7NzAR7Dmw

pythainlp.tokenize.newmm.segment(text: str, custom_dict: pythainlp.tokenize.trie.Trie = <pythainlp.tokenize.trie.Trie object>, safe_mode: bool = False) → List[str][source]¶: Dictionary-based maximal matching word segmentation, constrained with Thai Character Cluster boundaries. :param str text: text to be tokenized to words :param pythainlp.trie.Trie custom_dict: dictionary for tokenization :param bool safe_mode: True to help avoid long wait for text with long and continuous ambiguous breaking points. Long wait may still able to occur. Default is False. :return: list of words, tokenized from the text

longest¶

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

See Also

GitHub Repository

multi_cut¶

Multi cut – Thai word segmentation with maximum matching. The original source code is from Korakot Chaovavanich.

See Also

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

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

attacut¶

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

See Also

GitHub repository

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.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: subword (character cluster)
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¶

Enhanced Thai Character Cluster (ETCC) (In progress) Python implementation by Wannaphong Phatthiyaphaibun (19 June 2017)

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.