tltk 1.6.4

Thai Language Toolkit

TLTK is a Python package for Thai language processing: syllable, word, discouse unit segmentation, pos tagging, named entity recognition, grapheme2phoneme, ipa transcription, romanization, etc. TLTK requires Python 3.4 or higher. The project is a part of open source software developed at Chulalongkorn University. Since version 1.2.2 pacakge license is changed to New BSD License (BSD-3-Clause)

Input : must be utf8 Thai texts.

Updates:

Version 1.6.3 : fix bug on “g2p”, modify featues in “WordAna”, “TextAna”

Version 1.6.2 : change text features

Version 1.6.1 : change new text features, update Word2Vec model using ‘TNCc5model3.bin’, change g2p_all to th2ipa_all

Version 1.6 : add TNC_tag to mark up Thai text as XML format.

Version 1.5.8 : add average reduced frequency in TextAna

Version 1.5.7 : SylAna(syl,phone) is added. It is included in WordAna. The result is a list of syllables’ properties, which is added to the word property. th2read(text) is added. It show pronunciation in Thai written forms.

Version 1.5 : WordAna and TextAna are added. WordAna returns the output as an object with word properties.

res = tltk.nlp.TNC_tag(text,POS) => XML format of Thai texts as used in TNC. POS Option=”Y|N”

sp = tltk.nlp.SylAna(syl_form,syl_phone) => sp.form (syllable form), sp.phone(syllable phone), sp.char (No. of char in a syllable), sp.dead (syllable is dead or live T/F), sp.initC (initial consonant form), sp.finalC (final consonant form), sp.vowel (vowel form), sp.tonemark (เอก, โท, ตรี, จัตวา), sp.initPh (initial consonant sound), sp.finalPh (final consonant spund), sp.vowelPh (vowel sound), sp.tone (tone 1,2,3,4,5), sp.leading (leading syllable e.g. สบาย T/F), sp.cluster (T/F the syllable has an initial cluster), sp.karan (character marked with karan marker)

wd = tltk.nlp.WordAna(w) => wd.form (word form), wd.phone(word sound), wd.char (No. of char in a word), wd.syl (No. of syllables), wd.corrtone (No. of tone match the same tone marker), wd.corrfinal (No. of final consonant sound match the final character -ก -ด -ง -น -ม -ย -ว), wd.karan (No. of karan), wd.cluster (No. of cluster consonant), wd.lead (No. of leading consonant), wd.doubvowel (No. of complex vowel), wd.syl_prop (a list of syllables’ properties)

res = tltk.nlp.TextAna(text,TextOption,WordOption) => a complex dictionary output describing the input text.

TextOption = “segmented|edu|par” Use “segmented” if text is segmented with <p>,<s>, and |. Use “edu” for using TLTK edu segmentation. Use “par” for using “\n” in the text.

WordOption = “colloc|mm” If text is not yet segmented, use “colloc” or “mm” for word segment by TLTK.

### properties from SylAna

• form ## syllable form

• phone ## syllable sound

• char ## No. of char in a syllable

• dead = True|False ## dead or alive

• initC = ‘’ ## initial consonant

• finalC = ‘’ ## final consonant

• vowel = ‘’ ## vowel form

• tonemark ## tone marker 1,2,3,4,5

• initPh = ‘’ ## initial sound

• finalPh = ‘’ ## final sound

• vowelPh = ‘’ ## vowel sound

• tone ## tone 1,2,3,4,5

• leading = True|False ## leading syllable e.g. สบาย สห

• cluster = True|False ## cluster consonant

• karan = ‘’ ## character(s) marked by karan

### properties from WordAna

• form ## word form

• phone ## word sound

• char ## No. of chararcters

• syl ## No. of syllables

• corrtone ## No. of correct tone form and phone (สามัญ, ่ เอก, ้ โท, ๊ ตรี, ๋ จัตวา)

• incorrtone ## No. of incorrect tone form and phone

• corrfinal ## No. of correct final consonant -ก -ด -ง -น -ม -ย -ว

• incorrfinal ## No. of incorrect final consonant not(-ก -ด -ง -น -ม -ย -ว)

• karan ## No. of karan

• cluster ## No. of cluster

• lead ## No. of leading syllable

• doubvowel ## No. of double vowel

### properties from TextAna

• DesSpC ## No. of space in a text

• DesChaC ## No. of characters in a text

• DesSymbC ## No. of symbols or special characters in a text

• DesPC ## No. of paragraph

• DesEduC ## No. of edu unit

• DesTotW ## total word in a text

• DesTotT ## total type in a text

• DesEduL ## mean of edu length (word)

• DesEduLd ## standard deviation of edu length (word)

• DesWrdL ## mean of word length (syllable)

• DesWrdLd ## standard deviation of word length (syllable)

• DesPL ## mean of paragraph length (word)

• DesCorrToneC ## no. of words with the same tone form and phone

• DesInCorrToneC ## no. of words with the different tone form and phone

• DesCorrFinalC ## no. of words with correct final consonant -ก -ด -ง -น -ม -ย -ว

• DesInCorrFinalC ## no. of words with final consonant not -ก -ด -ง -น -ม -ย -ว

• DesClusterC ## no. of words with a cluster

• DesLeadC ## no. of words with a leading syllabe

• DesDoubVowelC ## no. of words with a double vowel

• DesTNCt1C ## No. word in TNC tier1 50%

• DesTNCt2C ## No. word in TNC tier2 51-60%

• DesTNCt3C ## No. word in TNC tier3 61-70%

• DesTNCt4C ## No. word in TNC tier4 71-80%

• DesTTC1 ## No. word in TTC level1

• DesTTC2 ## No. word in TTC level2

• DesTTC3 ## No. word in TTC level3

• DesTTC4 ## No. word in TTC level4

• WrdCorrTone ## ratio of word with the same tone form and phone

• WrdInCorrTone ## ratio of word with different tone form and phone

• WrdCorrFinal ## ratio of word with correct final consonant -ก -ด -ง -น -ม -ย -ว

• WrdInCorrFinal ## ratio of word with final consonant not -ก -ด -ง -น -ม -ย -ว

• WrdKaran ## ratio of word with a karan

• WrdCluster ## ratio of word with a cluster

• WrdLead ## ratio of word with a leading syllable

• WrdDoubVowel ## ratio of word with a double vowel

• WrdNEl ## ratio of named entity location

• WrdNEo ## ratio of named entity organization

• WrdNEp ## ratio of named entity person

• WrdNeg ## ratio of negation

• WrdTNCt1 ## relative frequency of words in TNC tier1 (/1000 words)

• WrdTNCt2 ## relative frequency of words in TNC tier2

• WrdTNCt3 ## relative frequency of words in TNC tier3

• WrdTNCt4 ## relative frequency of words in TNC tier4

• WrdTTC1 ## relative frequency of words in TTC level1

• WrdTTC2 ## relative frequency of words in TTC level2

• WrdTTC3 ## relative frequency of words in TTC level3

• WrdTTC4 ## relative frequency of words in TTC level4

• WrdC ## mean of relative frequency of content words in TTC

• WrdF ## mean of relative frequency of function words in TTC

• WrdCF ## mean of relative frequency of content/function words in TTC

• WrdFrmSing ## mean of relative frequency of single word forms in TTC

• WrdFrmComp ## mean of relative frequency of complex/compound word forms in TTC

• WrdFrmTran ## mean of relative frequency of transliteration words in TTC

• WrdSemSimp ## mean of relative frequency of simple word in TTC

• WrdSemTran ## mean of relative frequency of transparent compound word in TTC

• WrdSemSemi ## mean of relative frequency of words in between transparent and opaque compound in TTC

• WrdSemOpaq ## mean of relative frequency of opaque compound word in TTC

• WrdBaseM ## mean of relative frequency of basic vocab from ministry of education

• WrdBaseT ## mean of relative frequency of basic vocab from TTC & TNC < 2000

• WrdTfidf ## average of tfidf of each word (calculated from TNC)

• WrdTncDisp ## average of dispersion of each word (calculated from TNC)

• WrdTtcDisp ## average of dispersion of each word (calculated from TTC)

• WrdArf ## average of arf (average reduced frequency) of each word in the text

• WrdNOUN ## mean of relative frequency of word with POS=NOUN

• WrdVERB ## mean of relative frequency of word with POS=VERB

• WrdADV ## mean of relative frequency of word with POS=ADV

• WrdDET ## mean of relative frequency of word with POS=DET

• WrdADJ ## mean of relative frequency of word with POS=ADJ

• WrdADP ## mean of relative frequency of word with POS=ADP

• WrdPUNCT ## mean of relative frequency of word with POS=PUNCT

• WrdAUX ## mean of relative frequency of word with POS=AUX

• WrdSYM ## mean of relative frequency of word with POS=SYM

• WrdINTJ ## mean of relative frequency of word with POS=INTJ

• WrdCCONJ ## mean of relative frequency of word with POS=CCONJ

• WrdPROPN ## mean of relative frequency of word with POS=PROPN

• WrdNUM ## mean of relative frequency of word with POS=NUM

• WrdPART ## mean of relative frequency of word with POS=PART

• WrdPRON ## mean of relative frequency of word with POS=PRON

• WrdSCONJ ## mean of relative frequency of word with POS=SCONJ

• LdvTTR ## type/token ratio

• CrfCWL ## proportion of explicit content words overlapped locally (Y/N)

• CrfCTL ## proportion of explicit content words overlapped locally (no.tokens overlap)

• wrd ## wrd[word]=freq, dictionary of word and frequency

• wrd_arf ## wrd_arf[word]=arf, dictionary of word and average reduced frequency

Version 1.4 updated for gensim 4.0, Users can load a Thai corpus using Corpus(), then create a model using W2V_train(), D2V_train(); or load existing model from W2V_load(Model_File). TNC pretained w2v model is TNCc5model2.bin. Model of EDU segmentation is recompiled to work with new library.

Version 1.3.8 add spell_variants to gennerate all variation forms of the same pronunciation.

Version 1.3.6 remove “matplotlib” dependency, fix error on “ใคร”

More compound words are added in the dictionary. Version 1.1.3-1.1.5 have many entries that are not a word and contain a few errors. Those entries are removed in later versions.

NER tagger model was updated by using more NE data from AiforThai project.

tltk.nlp : basic tools for Thai language processing.

>tltk.nlp.TNC_tag(Text,POSTagOption) e.g. tltk.nlp.TNC_tag(‘นายกรัฐมนตรีกล่าวกับคนขับรถประจำทางหลวงสายสองว่า อยากวิงวอนให้ใช้ความรอบคอบ’,POS=’Y’)

=> ‘<w tran=”naa0jok3rat3tha1mon0trii0” POS=”NOUN”>นายกรัฐมนตรี</w><w tran=”klaaw1” POS=”VERB”>กล่าว</w><w tran=”kap1” POS=”ADP”>กับ</w><w tran=”khon0khap1rot3” POS=”NOUN”>คนขับรถ</w><w tran=”pra1cam0” POS=”NOUN”>ประจำ</w><w tran=”thaaN0luuaN4” POS=”NOUN”>ทางหลวง</w><w tran=”saaj4” POS=”NOUN”>สาย</w><w tran=”sOON4” POS=”NUM”>สอง</w><w tran=”waa2” POS=”SCONJ”>ว่า</w><s/><w tran=”jaak1” POS=”VERB”>อยาก</w><w tran=”wiN0wOOn0” POS=”VERB”>วิงวอน</w><w tran=”haj2” POS=”SCONJ”>ให้</w><w tran=”chaj3” POS=”VERB”>ใช้</w><w tran=”khwaam0” POS=”NOUN”>ความ</w><w tran=”rOOp2khOOp2” POS=”VERB”>รอบคอบ</w><s/>’

>tltk.nlp.chunk(Text) : chunk parsing. The output includes markups for word segments (|), elementary discourse units (<u/>), pos tags (/POS),and named entities (<NEx>…</NEx>), e.g. tltk.nlp.chunk(“สำนักงานเขตจตุจักรชี้แจงว่า ได้นำป้ายประกาศเตือนปลิงไปปักตามแหล่งน้ำ ในเขตอำเภอเมือง จังหวัดอ่างทอง หลังจากนายสุกิจ อายุ 65 ปี ถูกปลิงกัดแล้วไม่ได้ไปพบแพทย์”)

=> ‘<NEo>สำนักงาน/NOUN|เขต/NOUN|จตุจักร/PROPN|</NEo>ชี้แจง/VERB|ว่า/SCONJ|<s/>/PUNCT|ได้/AUX|นำ/VERB|ป้ายประกาศ/NOUN|เตือน/VERB|ปลิง/NOUN|ไป/VERB|ปัก/VERB|ตาม/ADP|แหล่งน้ำ/NOUN|<u/>ใน/ADP|<NEl>เขต/NOUN|อำเภอ/NOUN|เมือง/NOUN|<s/>/PUNCT|จังหวัด/NOUN|อ่างทอง/PROPN|</NEl><u/>หลังจาก/SCONJ|<NEp>นาย/NOUN|สุ/PROPN|กิจ/NOUN|</NEp><s/>/PUNCT|อายุ/NOUN|<u/>65/NUM|<s/>/PUNCT|ปี/NOUN|<u/>ถูก/AUX|ปลิง/VERB|กัด/VERB|แล้ว/ADV|ไม่ได้/AUX|ไป/VERB|พบ/VERB|แพทย์/NOUN|<u/>’

>tltk.nlp.ner_tag(Text) : The output includes markups for named entities (<NEx>…</NEx>), e.g. tltk.nlp.ner_tag(“สำนักงานเขตจตุจักรชี้แจงว่า ได้นำป้ายประกาศเตือนปลิงไปปักตามแหล่งน้ำ ในเขตอำเภอเมือง จังหวัดอ่างทอง หลังจากนายสุกิจ อายุ 65 ปี ถูกปลิงกัดแล้วไม่ได้ไปพบแพทย์”)

=> ‘<NEo>สำนักงานเขตจตุจักร</NEo>ชี้แจงว่า ได้นำป้ายประกาศเตือนปลิงไปปักตามแหล่งน้ำ ใน<NEl>เขตอำเภอเมือง จังหวัดอ่างทอง</NEl> หลังจาก<NEp>นายสุกิจ</NEp> อายุ 65 ปี ถูกปลิงกัดแล้วไม่ได้ไปพบแพทย์’

>tltk.nlp.ner([(w,pos),….]) : module for named entity recognition (person, organization, location), e.g. tltk.nlp.ner([(‘สำนักงาน’, ‘NOUN’), (‘เขต’, ‘NOUN’), (‘จตุจักร’, ‘PROPN’), (‘ชี้แจง’, ‘VERB’), (‘ว่า’, ‘SCONJ’), (’<s/>’, ‘PUNCT’)])

=> [(‘สำนักงาน’, ‘NOUN’, ‘B-O’), (‘เขต’, ‘NOUN’, ‘I-O’), (‘จตุจักร’, ‘PROPN’, ‘I-O’), (‘ชี้แจง’, ‘VERB’, ‘O’), (‘ว่า’, ‘SCONJ’, ‘O’), (’<s/>’, ‘PUNCT’, ‘O’)] Named entity recognition is based on crf model adapted from http://sklearn-crfsuite.readthedocs.io/en/latest/tutorial.html Model is trainned with a corpus containing 170,000 named entities. B-O, I-O are tags for organizations; B-P, I-P are tags for persons; and B-L, I-L are tags for locations.

>tltk.nlp.pos_tag(Text,WordSegmentOption) : word segmentation and POS tagging (using nltk.tag.perceptron), e.g. tltk.nlp.pos_tag(‘โปรแกรมสำหรับใส่แท็กหมวดคำภาษาไทย วันนี้ใช้งานได้บ้างแล้ว’) or

=> [[(‘โปรแกรม’, ‘NOUN’), (‘สำหรับ’, ‘ADP’), (‘ใส่’, ‘VERB’), (‘แท็ก’, ‘NOUN’), (‘หมวดคำ’, ‘NOUN’), (‘ภาษาไทย’, ‘PROPN’), (’<s/>’, ‘PUNCT’)], [(‘วันนี้’, ‘NOUN’), (‘ใช้งาน’, ‘VERB’), (‘ได้’, ‘ADV’), (‘บ้าง’, ‘ADV’), (‘แล้ว’, ‘ADV’), (’<s/>’, ‘PUNCT’)]]

By default word_segment(Text,”colloc”) will be used, but if option = “mm”, word_segment(Text,”mm”) will be used; POS tag set is based on Universal POS tags.. http://universaldependencies.org/u/pos/index.html nltk.tag.perceptron model is used for POS tagging. It is trainned with POS-tagged subcorpus in TNC (148,000 words)

>tltk.nlp.pos_tag_wordlist(WordLst) : Same as “tltk.nlp.pos_tag”, but the input is a word list, [w1,w2,…]

>tltk.nlp.segment(Text) : segment a paragraph into elementary discourse units (edu) marked with <u/> and segment words in each edu e.g. tltk.nlp.segment(“แต่อาจเพราะนกกินปลีอกเหลืองเป็นพ่อแม่มือใหม่ รังที่ทำจึงไม่ค่อยแข็งแรง วันหนึ่งรังก็ฉีกเกือบขาดเป็นสองท่อนห้อยต่องแต่ง ผมพยายามหาอุปกรณ์มายึดรังกลับคืนรูปทรงเดิม ขณะที่แม่นกกินปลีอกเหลืองส่งเสียงโวยวายอยู่ใกล้ ๆ แต่สุดท้ายไม่สำเร็จ สองสามวันต่อมารังที่ช่วยซ่อมก็พังไป ไม่เห็นแม่นกบินกลับมาอีกเลย”)

=> ‘แต่|อาจ|เพราะ|นกกินปลีอกเหลือง|เป็น|พ่อแม่|มือใหม่|<s/>|รัง|ที่|ทำ|จึง|ไม่|ค่อย|แข็งแรง<u/>วัน|หนึ่ง|รัง|ก็|ฉีก|เกือบ|ขาด|เป็น|สอง|ท่อน|ห้อย|ต่องแต่ง<u/>ผม|พยายาม|หา|อุปกรณ์|มา|ยึด|รัง|กลับคืน|รูปทรง|เดิม<u/>ขณะ|ที่|แม่|นกกินปลีอกเหลือง|ส่งเสียง|โวยวาย|อยู่|ใกล้|ๆ<u/>แต่|สุดท้าย|ไม่|สำเร็จ|<s/>|สอง|สาม|วัน|ต่อ|มา|รัง|ที่|ช่วย|ซ่อม|ก็|พัง|ไป<u/>ไม่|เห็น|แม่|นก|บิน|กลับ|มา|อีก|เลย<u/>’ edu segmentation is based on syllable input using RandomForestClassifier model, which is trained on an edu-segmented corpus (approx. 7,000 edus) created and used in Nalinee's thesis

>tltk.nlp.word_segment(Text,method=’mm|ngram|colloc’) : word segmentation using either maximum matching or ngram or maximum collocation approach. ‘colloc’ is used by default. Please note that the first run of ngram method would take a long time because TNC.3g will be loaded for ngram calculation. e.g.

>tltk.nlp.word_segment(‘ผู้สื่อข่าวรายงานว่านายกรัฐมนตรีไม่มาทำงานที่ทำเนียบรัฐบาล’) => ‘ผู้สื่อข่าว|รายงาน|ว่า|นายกรัฐมนตรี|ไม่|มา|ทำงาน|ที่|ทำเนียบรัฐบาล|<s/>’

>tltk.nlp.syl_segment(Text) : syllable segmentation using 3gram statistics e.g. tltk.nlp.syl_segment(‘โปรแกรมสำหรับประมวลผลภาษาไทย’)

=> ‘โปร~แกรม~สำ~หรับ~ประ~มวล~ผล~ภา~ษา~ไทย<s/>’

>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(‘คนขับรถประจำทางปรับอากาศ”’,10)

=> [[‘คนขับ|รถประจำทาง|ปรับอากาศ’, ‘คนขับรถ|ประจำทาง|ปรับอากาศ’, ‘คน|ขับ|รถประจำทาง|ปรับอากาศ’, ‘คน|ขับรถ|ประจำทาง|ปรับอากาศ’, ‘คนขับ|รถ|ประจำทาง|ปรับอากาศ’, ‘คนขับรถ|ประจำ|ทาง|ปรับอากาศ’, ‘คนขับ|รถประจำทาง|ปรับ|อากาศ’, ‘คนขับรถ|ประจำทาง|ปรับ|อากาศ’, ‘คน|ขับ|รถ|ประจำทาง|ปรับอากาศ’, ‘คนขับ|ร|ถ|ประจำทาง|ปรับอากาศ’]]

>tltk.nlp.g2p(Text) : return Word segments and pronunciations e.g. tltk.nlp.g2p(“สถาบันอุดมศึกษาไม่สามารถก้าวให้ทันการเปลี่ยนแปลงของตลาดแรงงาน”)

=> “สถา~บัน~อุ~ดม~ศึก~ษา|ไม่|สา~มารถ|ก้าว|ให้|ทัน|การ|เปลี่ยน~แปลง|ของ|ตลาด~แรง~งาน<tr/>sa1’thaa4~ban0~?u1~dom0~sUk1~saa4|maj2|saa4~maat2|kaaw2|haj2|than0|kaan0|pliian1~plxxN0|khOON4|ta1’laat1~rxxN0~Naan0|<s/>”

>tltk.nlp.th2ipa(Text) : return Thai transcription in IPA forms e.g. tltk.nlp.th2ipa(“ลงแม่น้ำรอเดินไปหาปลา”)

=> ‘loŋ1 mɛː3.naːm4 rᴐː1 dɤːn1 paj1 haː5 plaː1 <s/>’

>tltk.nlp.th2roman(Text) : return Thai romanization according to Royal Thai Institute guideline. .e.g. tltk.nlp.th2roman(“คือเขาเดินเลยลงไปรอในแม่น้ำสะอาดไปหามะปราง”)

=> ‘khue khaw doen loei long pai ro nai maenam sa-at pai ha maprang <s/>’

>tltk.nlp.th2read(Text) : convert text into Thai reading forms, e.g. th2read(‘สามารถเขียนคำอ่านภาษาไทยได้’)

=> ‘สา-มาด-เขียน-คัม-อ่าน-พา-สา-ไท-ด้าย-’

>tltk.nlp.th2ipa_all(Text) : return all transcriptions (IPA) as a list of tuple (syllable_list, transcription). Transcription is based on syllable reading rules. It could be different from th2ipa. e.g. tltk.nlp.th2ipa_all(“รอยกร่าง”)

=> [(‘รอย~กร่าง’, ‘rᴐːj1.ka2.raːŋ2’), (‘รอย~กร่าง’, ‘rᴐːj1.kraːŋ2’), (‘รอ~ยก~ร่าง’, ‘rᴐː1.jok4.raːŋ3’)]

>tltk.nlp.spell_candidates(Word) : list of possible correct words using minimum edit distance, e.g. tltk.nlp.spell_candidates(‘รักษ’)

=> [‘รัก’, ‘ทักษ’, ‘รักษา’, ‘รักษ์’]

>tltk.nlp.spell_variants(Word,InDict=”no|yes”,Karan=”exclude|include”) : list of word variants with the same pronunciation. Add option InDict = “yes” to save only words found in the dictionary. Use option Karan=’inlcude’ to include words spelling with the karan character. By default, InDict=”no” and Karan = “exclude”, e.g. tltk.nlp.spell_variants(‘โควิด’)

=> [‘โฆวิธ’, ‘โฆวิต’, ‘โฆวิด’, ‘โฆวิท’, ‘โฆวิช’, ‘โฆวิจ’, ‘โฆวิส’, ‘โฆวิษ’, ‘โฆวิตร’, ‘โฆวิฒ’, ‘โฆวิฏ’, ‘โฆวิซ’, ‘โควิธ’, ‘โควิต’, ‘โควิด’, ‘โควิท’, ‘โควิช’, ‘โควิจ’, ‘โควิส’, ‘โควิษ’, ‘โควิตร’, ‘โควิฒ’, ‘โควิฏ’, ‘โควิซ’]

Other defined functions in the package: >tltk.nlp.reset_thaidict() : clear dictionary content >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

tltk.corpus : basic tools for corpus enquiry

>tltk.corpus.Corpus_build(DIR,filetype=”xxx”) create a corpus as a list of paragraphs from files in DIR. The default file type is .txt Files have to be word-segmented before, e.g. w1|w2|w3|w4 …

>tltk.corpus.Corpus() create an object which has three methods for a copus: x.frequency(Text) x.dispersion(C) x.totalword(C). C is the result created from Corpus_build >C = tltk.corpus.Copus_build(‘temp/data/’) >corp = tltk.corpus.Corpus() >print(corp.frequency(C)) > {‘จังหวัด’: 32, ‘สมุทรสาคร’: 16, ‘เปิด’: 3, ‘ศูนย์’: 13, ‘ควบคุม’: 13, ‘แจ้ง’: 16, …..}

>tltk.corpus.Xwordlist() create an object which is a comparison of two wordlists A and B. Four comparison methods are defined: onlyA, onlyB, intersect, union. A and B is an object created from Corp.frequency(). Corp is an object created from Corpus() e.g. Xcomp.onlyA(c1.frequency(parsA),c2.frequency(parsB))) c1 = Corpus(); c2 = Corpus(); Xcomp = Xwordlist(); parsA and parsB are created from Corpus_build(…)

>tltk.corpus.W2V_train(Corpus) create a model of Word2Vec. Input is a corpus creted from Corpus_build.

>tltk.corpus.D2V_train(Corpus) create a model of Doc2Vec. Input is a corpus creted from Corpus_build.

>tltk.corpus.TNC_load() by default load TNC.3g. The file can be in the working directory or TLTK package directory

>tltk.corpus.trigram_load(TRIGRAM) ### load Trigram data from other sourse saved in tab delimited format “W1tW2tW3tFreq” 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 e.g. tltk.corpus.bigram(“หาย”,”ดี”) => 2.331959592765809

>tltk.corpus.trigram(w1,w2,w3) return frequency/million of Trigram w1-w2-w3 from the corpus

>tltk.corpus.collocates(w, stat=”chi2”, direct=”both”, span=2, limit=10, minfq=1) ### return all collocates of w, STAT = {freq,mi,chi2} DIR={left,right,both} SPAN={1,2} The output is a list of tuples ((w1,w2), stat). e.g. tltk.corpus.collocates(“วิ่ง”,limit=5)

=> [((‘วิ่ง’, ‘แจ้น’), 86633.93952758134), ((‘วิ่ง’, ‘ตื๋อ’), 77175.29122642518), ((‘วิ่ง’, ‘กระหืดกระหอบ’), 48598.79465339733), ((‘วิ่ง’, ‘ปรู๊ด’), 41111.63720974819), ((‘ลู่’, ‘วิ่ง’), 33990.56839021914)]

>tltk.corpus.W2V_load(File) load w2v model created from gensim. If no file is given, file “TNCc5modesl.bin” will be loaded.

>tltk.corpus.w2v_load() by deafult load word2vec file “TNCc5model2.bin”. The file can be in the working directory or TLTK package directory

>tltk.corpus.w2v_exist(w) check whether w has a vector representation e.g. tltk.corpus.w2v_exist(“อาหาร”) => True

>tltk.corpus.w2v(w) return vector representation of w

>tltk.corpus.similarity(w1,w2) e.g. tltk.corpus.similarity(“อาหาร”,”อาหารว่าง”) => 0.783551877546

>tltk.corpus.similar_words(w, n=10, cutoff=0., score=”n”) e.g. tltk.corpus.similar_words(“อาหาร”,n=5, score=”y”)

=> [(‘อาหารว่าง’, 0.7835519313812256), (‘ของว่าง’, 0.7366500496864319), (‘ของหวาน’, 0.703102707862854), (‘เนื้อสัตว์’, 0.6960341930389404), (‘ผลไม้’, 0.6641997694969177)]

>tltk.corpus.outofgroup([w1,w2,w3,…]) e.g. tltk.corpus.outofgroup([“น้ำ”,”อาหาร”,”ข้าว”,”รถยนต์”,”ผัก”]) => “รถยนต์”

>tltk.corpus.analogy(w1,w2,w3,n=1) e.g. tltk.corpus.analogy(‘พ่อ’,’ผู้ชาย’,’แม่’) => [‘ผู้หญิง’]

>tltk.corpus.w2v_plot([w1,w2,w3,…]) => plot a scratter graph of w1-wn in two dimensions

>tltk.corpus.w2v_compare_color([w1,w2,w3,…]) => visualize the components of vectors w1-wn in color

Notes

• 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 shortest meaningful words (i.e. ‘รถ|โดยสาร’, ‘คน|ใช้’, ‘กลาง|คืน’, ‘ต้น|ไม้’ 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 shortest 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 65,000 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.

• ‘setence segment’ or actually ‘edu segment’ is a process to break a paragraph into a chunk of discourse units, which usually are a clause. It is based on RandomForestClassifier model, which is trained on an edu-segmented corpus (approx. 7,000 edus) created and used in Nalinee’s thesis (http://www.arts.chula.ac.th/~ling/thesis/2556MA-LING-Nalinee.pdf). Accuracy of the model is 97.8%. The reason behind using edu can be found in [Aroonmanakun, W. 2007. Thoughts on Word and Sentence Segmentation in Thai. In Proceedings of the Seventh Symposium on Natural Language Processing, Dec 13-15, 2007, Pattaya, Thailand. 85-90.] [Intasaw, N. and Aroonmanakun, W. 2013. Basic Principles for Segmenting Thai EDUs. in Proceedings of 27th Pacific Asia Conference on Language, Information, and Computation, pages 491-498, Nov 22-24, 2013, Taipei.]

• ‘grapheme to phoneme’ (g2p), as well as IPA transcription (th2ipa) and Thai romanization (th2roman) is based on the hybrid approach presented in the paper “A Unified Model of Thai Romanization and Word Segmentation”. The Thai Royal Institute guidline for Thai romanization can be downloaded from “http://www.arts.chula.ac.th/~ling/tts/ThaiRoman.pdf”, or “http://www.royin.go.th/?page_id=619” [Aroonmanakun, W., and W. Rivepiboon. 2004. A Unified Model of Thai Word Segmentation and Romanization. In Proceedings of The 18th Pacific Asia Conference on Language, Information and Computation, Dec 8-10, 2004, Tokyo, Japan. 205-214.] (http://www.aclweb.org/anthology/Y04-1021)

Remarks

• TNC Trigram data (TNC.3g) and TNC word2vec (TNCc5model.bin) can be downloaded from TNC website. http://www.arts.chula.ac.th/ling/tnc/searchtnc/

• Module “spell_candidates” is modified from Peter Norvig’s Python codes at http://norvig.com/spell-correct.html

• Module “w2v_compare_color” is modified from http://chrisculy.net/lx/wordvectors/wvecs_visualization.html

• BEST corpus is the corpus released by NECTEC (https://www.nectec.or.th/corpus/)

• Universal POS tags are used in this project. For more information, please see http://universaldependencies.org/u/pos/index.html and http://www.arts.chula.ac.th/~ling/contents/File/UD%20Annotation%20for%20Thai.pdf

• pos_tag is based on PerceptronTagger in nltk.tag.perceptron. It is trained with TNC data manually pos-taged (approx. 148,000 words). Accuracy on pos tagging is 91.68%. NLTK PerceptronTagger is a port of the Textblob Averaged Perceptron Tagger, which can be found at https://explosion.ai/blog/part-of-speech-pos-tagger-in-python

• named entiy recognition module is a CRF model adapted from this tutorial (http://sklearn-crfsuite.readthedocs.io/en/latest/tutorial.html). The model is trained with NER data used in Sasimimon’s and Nutcha’s theses (altogether 7,354 names in a corpus of 183,300 words). (http://pioneer.chula.ac.th/~awirote/Data-Nutcha.zip, http://pioneer.chula.ac.th/~awirote/ Data-Sasiwimon.zip) and NER data from AIforThai (https://aiforthai.in.th/) Only valid NE files from AIforThai are used. The total number of all NEs is 170,076. Accuracy of the model is reported below (88%).

tag	precision	recall	f1-score	support
B-L	0.56	0.48	0.52	27105
B-O	0.72	0.58	0.64	59613
B-P	0.82	0.83	0.83	83358
I-L	0.52	0.43	0.47	17859
I-O	0.67	0.59	0.63	67396
I-P	0.85	0.88	0.86	175069
O	0.92	0.94	0.93	1032377
accuracy			0.88	1462777
macro avg	0.72	0.68	0.70	1462777
weighted avg	0.87	0.88	0.88	1462777

Use cases

This package is free for commercial use. If you incoporate this package in your work, we’d appreciate that you informed us through [e-mail]

• BAS Web Services (https://clarin.phonetik.uni-muenchen.de/BASWebServices/interface) used TLTK to do Thai grapheme to phoneme in their project.

• Chubb Life Assurance Public Company Limited used TLTK to do Thai transliteration.

• The .NET project wraps Thai Romanization in Thai Language Toolkit Project to simplify usage in other .NET projects. https://github.com/dotnetthailand/ThaiRomanizationSharp

• Huawei, Consumer Cloud Service Asia Pacific Cloud Service Business Growth Dept. used TLTK for AppSearch processing for Thai.