tltk 0.3.4

Thai Language Toolkit

Thai Language Toolkit Project
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TLTK is a Python package for Thai language processing. TLTK requires Python 3.4 or higher.
The project is a part of open source software provided by Chulalongkorn University.

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tltk.nlp : basic tools for Thai language processing.

Input : must be utf8 Thai texts.

List of basic tools:
>> tltk.nlp.pos_tag(Text,WordSegmentOption) : word segmentation and POS tagging (using nltk.tag.perceptron), e.g. pos_tag('โปรแกรมสำหรับประมวลผลภาษาไทย วันนี้ใช้งานได้แล้ว') or pos_tag('โปรแกรมสำหรับประมวลผลภาษาไทย',"mm") => [[('โปรแกรม', 'NOUN'), ('สำหรับ', 'ADP'), ('ประมวล', 'VERB'), ('ผล', 'NOUN'), ('ภาษา', 'NOUN'), ('ไทย', 'PROPN')], [('วันนี้', 'NOUN'), ('ใช้งาน', 'VERB'), ('ได้', 'ADV'), ('แล้ว', 'ADV')]]
By default word_segment() will be used, but if option = "mm", word_segment_mm() will be used;

>> tltk.nlp.pos_load() : load Thai tagger model before using pos_tag(). Universal POS tags are used here. http://universaldependencies.org/u/pos/index.html

>> tltk.nlp.word_sement(Text) : word segmentation using maximum collocation approach, e.g. word_segment('โปรแกรมสำหรับประมวลผลภาษาไทย') => 'โปรแกรม|สำหรับ|ประมวล|ผล|ภาษา|ไทย<s/>'

>> tltk.nlp.syl_segment(Text) : syllable segmentation using 3gram statistics e.g. syl_segment('โปรแกรมสำหรับประมวลผลภาษาไทย') => 'โปร~แกรม~สำ~หรับ~ประ~มวล~ผล~ภา~ษา~ไทย<s/>'

>> tltk.nlp.word_segment_mm(Text) : another word segmentation using maximal matching approach (minimum word approach)

>> tltk.nlp.word_segment_nbest(Text, N) : return the best N segmentations based on the assumption of minimum word approach. e.g. tltk.nlp.word_segment_nbest('คนขับรถประจำทางหลวง',10) => [['คนขับรถ|ประจำ|ทางหลวง', 'คนขับ|รถประจำทาง|หลวง', 'คนขับรถ|ประจำทาง|หลวง', 'คน|ขับรถ|ประจำ|ทางหลวง', 'คนขับ|รถ|ประจำ|ทางหลวง', 'คนขับรถ|ประ|จำ|ทางหลวง', 'คน|ขับ|รถประจำทาง|หลวง', 'คน|ขับรถ|ประจำทาง|หลวง', 'คนขับ|รถ|ประจำทาง|หลวง', 'คนขับรถ|ประจำ|ทาง|หลวง']]

>> tltk.nlp.spell_candidates(Word) : list of possible correct words using minimum edit distance, e.g. spell_candidates('รักษ') => ['รัก', 'ทักษ', 'อักษ', 'รุกษ', 'รักข', 'รักษา', 'รักษ์']


Other defined functions in the package:
>> tltk.nlp.reset_thaidict() : clear dictionary content setup.cfg
>> tltk.nlp.read_thaidict(DictFile) : add a new dictionary e.g. tltk.nlp.read_thaidict('BEST.dict')
>> tltk.nlp.check_thaidict(Word) : check whether Word exists in the dictionary

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tltk.corpus : basic tools for corpus enquiry

>> tltk.corpus.load3gram(TRIGRAM) ### load Trigram data from other sourse saved in tab delimited format "W1\tW2\tW3\tFreq" e.g. tltk.corpus.load3gram('TNC.3g') 'TNC.3g' can be downloaded separately from Thai National Corpus Project.
>> tltk.corpus.unigram(w1) ### return normalized frequecy (frequency/million) of w1 from the corpus
>> tltk.corpus.bigram(w1,w2) ### return frequency/million of Bigram w1-w2 from the corpus
>> tltk.corpus.trigram(w1,w2,w3) ### return frequency/million of Trigram w1-w2-w3 from the corpus
>> tltk.corpus.collocates(w,STAT,DIR,SPAN,LIMIT,MINFQ) ### return all collocates of w, STAT = {freq,mi,chi2} DIR={left,right,both} SPAN={1,2} LIMIT is the number of top collocates MINFQ is the minimum raw frequency of bigram w-x or x-w. The output is a list of tuples ((w1,w2), stat)

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Word segmentation is based on a maximum collocation approach described in this publication:
"Aroonmanakun, W. 2002. Collocation and Thai Word Segmentation. In Thanaruk Theeramunkong and Virach Sornlertlamvanich, eds. Proceedings of the Fifth Symposium on Natural Language Processing & The Fifth Oriental COCOSDA Workshop. Pathumthani: Sirindhorn International Institute of Technology. 68-75." (http://pioneer.chula.ac.th/~awirote/ling/SNLP2002-0051c.pdf)

Use tltk.nlp.word_segment(Text) or tltk.nlp.syl_segment(Text) for segmenting Thai texts. Syllable segmentation now is based on a trigram model trainned on 3.1 million syllable corpus. Input text is a paragraph of Thai texts which can be mixed with English texts. Spaces in the paragraph will be marked as "<s/>". Word boundary is marked by "|". Syllable boundary is marked by "~". Syllables here are written syllables. One written syllable may be pronounced as two syllables, i.e. "สกัด" is segemnted here as one written syllable, but it is pronounced as two syllables "sa1-kat1".

Determining words in a sentence is based on the dictionary and maximum collocation strength between syllables. Since many compounds and idioms, e.g. 'เตาไมโครเวฟ', 'ไฟฟ้ากระแสสลับ', 'ปีงบประมาณ', 'อุโมงค์ใต้ดิน', 'อาหารจานด่วน', 'ปูนขาวผสมพิเศษ', 'เต้นแร้งเต้นกา' etc., are included in the standard dictionary, these will likely be segmented as one word. For applications that prefer smallest meaningful words (i.e. 'รถ','โดยสาร' as segmented in BEST corpus), users should reset the default dictionary used in this package and reload a new dictionary containing only simple words or smallest meaningful words. Use "reset_thaidict()" to clear default dictionary content, and "read_thaidict('DICT_FIILE')" to load a new dictionary. A list of words compiled from BEST corpus is included in this package as a file 'BEST.dict'

The standard dictionary used in this package has more then 40,000 entries including abbreviations and transliterations compiled from various sources. A dictionary of 8,700 proper names e.g. country names, organization names, location names, animal names, plant names, food names, ..., such as 'อุซเบกิสถาน', 'สำนักเลขาธิการนายกรัฐมนตรี', 'วัดใหญ่สุวรรณาราม', 'หนอนเจาะลำต้นข้าวโพด', 'ปลาหมึกกระเทียมพริกไทย', are also added as a list of words in the system.

For segmenting a specific domain text, a specialized dicionary can be used by adding more dictionary before segmenting texts. This can be done by calling read_thaidict("SPECIALIZED_DICT"). Please note that the dictionary is a text file in "iso-8859-11" encoding. The format is one word per one line.

*** Module "spell_candidates" is modified from Peter Norvig's Python codes at http://norvig.com/spell-correct.html ***
*** BEST corpus is the corpus released by NECTEC (https://www.nectec.or.th/corpus/) ***
*** pos_tag is based on PerceptronTagger in nltknltk.tag.perceptron. More information can be found at https://explosion.ai/blog/part-of-speech-pos-tagger-in-python ***
*** Universal POS tags are used in this project. For more information, please see http://universaldependencies.org/u/pos/index.html ***

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More basic tools will be added in the future, e.g.
#### tltk.nlp.transcribe(ThaiText) => IPA
#### tltk.nlp.pronounce(ThaiWord) สหกิจ => สะหะกิด