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20190407 第7章 事例研究:自然言語処理における素性

20190407 第7章 事例研究:自然言語処理における素性

本郷言語処理勉強会 #3 で用いた資料。
https://holy.connpass.com/event/122295/
「ゴールドバーグ, 『自然言語処理のための深層学習』, 共立出版, 2019」輪読会。

kmotohas

April 07, 2019
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  1. !LNPUPIBT ຊڷݴޠॲཧษڧձ ୈ7ষ ࣄྫݚڀɿࣗવݴޠॲཧʹ͓͚Δૉੑ

  2. ࣗݾ঺հ !2  ຊڮ࿨و !LNPUPIBT  ‣ ౦޻େجૅ෺ཧֶઐ߈1I%  

    ‣ ιϑτόϯΫגࣜձࣾ   ‣ %&&1$03&*OD   ‣ εΧΠϚΠϯυגࣜձࣾ   w ਂ૚ֶशϞσϧͷ։ൃɾӡ༻ϓϥοτϑΥʔϜl4,*-z࡞ͬͯΔձࣾ w +BWB4DBMBͰͰ͖ΔEFFQMFBSOJOHKͷ։ൃ w ,FSBTOE UI DPOUSJCVUPST͕͍ͨΓ͢Δ w ๻͸ϓϦηʔϧεΤϯδχΞ
  3. ໨࣍ !3  ୈ̎ฤɹࣗવݴޠσʔλͷѻ͍ ‣ ୈ̒ষɹςΩετσʔλͷͨΊͷૉੑ ‣ ୈ̓ষɹࣄྫݚڀɿࣗવݴޠॲཧʹ͓͚Δૉੑ w จॻ෼ྨɿݴޠಉఆ

    w จॻ෼ྨɿτϐοΫ෼ྨ w จॻ෼ྨɿஶऀಛఆ w จ຺ʹຒΊࠐ·Εͨ୯ޠɿ඼ࢺλά෇͚ w จ຺ʹຒΊࠐ·Εͨ୯ޠɿݻ༗දݱೝࣝ w จ຺ʹຒΊࠐ·Εͨ୯ޠͱݴޠֶతૉੑɿલஔࢺҙຯᐆດੑղফ w จ຺ʹຒΊࠐ·Εͨ୯ޠͷؒͷؔ܎ɿΞʔΫΛ୯Ґͱͨ͠ύʔδϯά
  4. จॻ෼ྨɿݴޠಉఆ !4  จࣈόΠάϥϜͷόοάʢCBHPGMFUUFSCJHSBNTʣ͕ڧྗ @btsmith #nlp ▪ Character n-gram frequencies

    for English Language Identification 28 e 12.6% t 9.1% a 8.0% o 7.6% i 6.9% n 6.9% s 6.3% h 6.2% … th 3.9% he 3.7% in 2.3% er 2.2% an 2.1% re 1.7% nd 1.6% on 1.4% … the 3.5% and 1.6% ing 1.1% her 0.8% hat 0.7% his 0.6% tha 0.6% ere 0.6% … From Cryptograms.org, derived from English documents at Project Gutenberg https://www.slideshare.net/LithiumTech/lightweight-natural-language-processing-nlp
  5. จॻ෼ྨɿݴޠಉఆ !5  จࣈූ߸ԽํࣜಉఆʢFODPEJOHEFUFDUJPOʣʹ͸όΠτόΠάϥϜͷόοά͕༗ޮ Figure 2: Byte-based method vs. character-based

    method – ISO-2022-{JP,KR} [ja,ko] – UTF-8 [universal] or characters (unigram models can use two or ters (bigrams, trigram parameter space is exp between the accuracy ing, computation and s tant as the size of the Asian charsets with ch 3.3 Algorithm Our first choice was N http://cs229.stanford.edu/proj2007/KimPark-AutomaticDetectionOfCharacterEncodingAndLanguages.pdf
  6. จॻ෼ྨɿτϐοΫ෼ྨ !6  จষ㱨\ܦࡁ੓࣏εϙʔπϨδϟʔΰγοϓੜ׆ͦͷଞ^ʁ  ҙຯతͳجຊతͳ୯Ґ͸୯ޠ
 ʢޠॱ͸͋·ΓҙຯΛ࣋ͨͳ͍ʣ ‣ ୯ޠͷόοά CBDLPGXPSET

     ‣ ୯ޠόΠάϥϜͷόοά CBHPGXPSECJHSBNT  w ϓϥεͰɺϨϯϚԽɺ୯ޠຒΊࠐΈϕΫτϧɺ5'*%'ॏΈ෇͚ͳͲ΋ޮՌ͋Γ https://aylien.com/text-api/text-classification/
  7. จॻ෼ྨɿஶऀಛఆ !7  ஶऀ໊ɾੑผɾ೥ྸɾ฼ޠͳͲɺஶऀͷಛ௃Λਪఆ  จষͷελΠϧʹؔ͢Δಛ௃͕༗༻ ‣ ඼ࢺλάͱػೳޠ GVODUJPOXPSE 

    w ػೳޠɿPO PG UIF BOE CFGPSF ʜIF TIF * UIFZ ʜ w ͦΕࣗ਎͸಺༰Λ఻͑ͣɺ಺༰Λ఻͑Δ୯ޠͱ݁ͼ͍ͭͯҙຯΛׂΓ౰ͯΔ w େن໛ίʔύεͷ࠶සग़୯ޠ্Ґޠఔ౓͕ۙࣅతʹػೳޠͷϦετʹͳΔ w ͦΕͧΕͷCJHSBN USJHSBN HSBN ػೳޠͷີ౓ͳͲ͕࢖͑Δ
  8. จॻ෼ྨɿஶऀಛఆ !8  ྫɿl#VJMEJOH.BDIJOF-FBSOJOH4ZTUFNTXJUI1ZUIPOzͷ֤ষͷஶऀಛఆ ‣ CZ8JMMJ3JDIFSUBOE-VJT1FESP$PFMIP  ڭࢣͳֶ͠शͷख๏Λར༻ͯ͠શষͷஶऀಛఆͰશ໰ਖ਼ղ ‣ Ұจ͋ͨΓͷฏۉ୯ޠ਺

    ‣ Ұจͷ௕͞ͷ෼ࢄ ‣ ޠኮ਺ ‣ Ұจ͋ͨΓͷΧϯϚɺηϛίϩϯɺίϩϯͷฏۉ਺ ‣ ػೳޠͷόοά ‣ ֤඼ࢺλάͷग़ݱׂ߹  ͜ΕΒͷಛ௃ྔΛ࢖ͬͯLNFBOT L ΫϥελϦϯά http://www.aicbt.com/authorship-attribution/
  9. จ຺ʹຒΊࠐ·Εͨ୯ޠɿ඼ࢺλά෇͚ !9  ඼ࢺλά෇͚ QBSUTPGTQFFDIUBHHJOH  w ྫ 6OJWFSTBM5SFFCBOL1SPKFDU ܗ༰ࢺɺ઀ஔࢺɺ෭ࢺɺॿಈࢺɺ౳Ґ઀ଓࢺɺݶఆࢺɺؒ౤ࢺɺ

    ໊ࢺɺ਺ࢺɺෆมԽࢺɺ୅໊ࢺɺݻ༗໊ࢺɺ۟ಡ఺ɺैଐ઀ଓࢺɺه߸ɺಈࢺɺͦͷଞ ‣ ߏ଄໰୊ɺ·ͨ͸྆ଆ̎୯ޠͷ૭ʹ͓͚Δ඼ࢺλά෼ྨͷλεΫʹۙࣅ w JOUSJOTJDʢ୯ޠͦΕࣗମʹجͮ͘ʣख͕͔Γ  ୯ޠͦΕࣗ਎ɺ઀಄ࣙɺ઀ඌࣙɺܗঢ়ʢFEɺVOɺେจࣈʣɺग़ݱස౓ w FYUSJOTJDʢͦͷจ຺ʹجͮ͘ʣख͕͔Γ  पΓͷ୯ޠͷɺ୯ޠͦΕࣗ਎ɺ઀಄ࣙɺ઀ඌࣙɺલޙͷ඼ࢺ༧ଌ݁Ռ
  10. จ຺ʹຒΊࠐ·Εͨ୯ޠɿݻ༗දݱೝࣝ !10 タスク 説明: 固有表現抽出 • テキスト 中から人名や組織名 ような固有表現 (NE)

    を抽出する • 固有表現 定義 ほしい情報に基づいて柔軟に定義する • 情報抽出や検索エンジン インデキシングなどで有用 https://explosion.ai/demos/displacy-ent 5  https://speakerdeck.com/himkt/neural-named-entity-recognition
  11. จ຺ʹຒΊࠐ·Εͨ୯ޠɿݻ༗දݱೝࣝ !11  ܥྻϥϕϦϯά໰୊ TFRVFODFMBCFMJOHUBTL ͱͯ͠ఆࣜԽ ‣ ϥϕϧ෇͚ͷεΩʔϚ#*0λά #FHJO*OTJEF0VUTJEFFODPEFEUBH 

     $"4& 456%*&4 0' /-1 '&"563&4 ɩF TFOUFODF BCPWF XPVME CF UBHHFE BT CQ?Mf"@S1_ aKBi?fA@S1_ -fP T`2bB/2MifP Q7fP J+*Q`KBFf"@P_: AM/mbi`B2bfA@P_: pBbBi2/fP ?BbfP MB2+2fP S`Bbf"@S1_ BMfP JBHMf"@GP* -fP `2TQ`i2`bfP bvfP XfP ɩF USBOTMBUJPO GSPN OPOPWFSMBQQJOH TFHNFOUT UP #*0 UBHT BOE CBDL JT TUSBJHIUGPSXBSE -JLF 104UBHHJOH UIF /&3 UBTL JT B TUSVDUVSFE POF BT UBHHJOH EFDJTJPOT GPS EJĊFSFOU XPSET JOUFSBDU XJUI FBDI PUIFS JU JT NPSF MJLFMZ UP SFNBJO XJUIJO UIF TBNF FOUJUZ UZQF UIBO UP TXJUDI JU JT NPSF MJLFMZ UP UBH i+PIO 4NJUI *ODw BT "@P_: A@P_: A@P_: UIBO BT "@S1_ A@S1_ "@P_:  )PXFWFS XF BHBJO BTTVNF JU DBO CF BQQSPYJNBUFE SFBTPOBCMZ XFMM VTJOH JOEFQFOEFOU DMBTTJmDBUJPO EFDJTJPOT ɩF DPSF GFBUVSF TFU GPS UIF /&3 UBTL JT TJNJMBS UP UIBU PG UIF 104UBHHJOH UBTL BOE SFMJFT PO XPSET XJUIJO B XPSET XJOEPX UP FBDI TJEF PG UIF GPDVT XPSE *O BEEJUJPO UP UIF GFBUVSFT PG FOUJUJFT TVDI BT .JMBO +PIO 4NJUI .D$PSNJL *OEVTUSJFT BOE 1BSJT BT XFMM BT UP DBUFHPSJ[F UIFN JOUP B QSFEFmOFE TFU PG DBUFHPSJFT TVDI BT -ĠĔĒĥĚĠğ 0ģĘĒğĚīĒĥĚĠğ 1ĖģĤĠğ PS 0ĥęĖģ /PUF UIBU UIJT UBTL JT DPOUFYU EFQFOEFOU BT .JMBO DBO CF B MPDBUJPO UIF DJUZ PS BO PSHBOJ[BUJPO B TQPSUT UFBN i.JMBO QMBZFE BHBJOTU #BSTB 8FEOFTEBZ FWFOJOHw BOE 1BSJT DBO CF UIF OBNF PG B DJUZ PS B QFSTPO " UZQJDBM JOQVU UP UIF QSPCMFN XPVME CF B TFOUFODF TVDI BT CQ?M aKBi? - T`2bB/2Mi Q7 J+*Q`KBF AM/mbi`B2b pBbBi2/ ?Bb MB2+2 S`Bb BM JBHM - `2TQ`i2`b bv X BOE UIF FYQFDUFE PVUQVU XPVME CF (S1_ CQ?M aKBi? ) - T`2bB/2Mi Q7 (P_: J+*Q`KBF AM/mbi`B2b ) pBbBi2/ ?Bb MB2+2 (S1_ S`Bb ) BM (GP* JBHM )- `2TQ`i2`b bv X 8IJMF /&3 JT B TFRVFODF TFHNFOUBUJPO UBTL‰JU BTTJHOT MBCFMFE CSBDLFUT PWFS OPO PWFSMBQQJOH TFOUFODF TQBOT‰JU JT PGUFO NPEFMFE BT B TFRVFODF UBHHJOH UBTL MJLF 104UBHHJOH ɩF VTF PG UBHHJOH UP TPMWF TFHNFOUBUJPO UBTLT JT QFSGPSNFE VTJOH #*0 FODPEFE UBHTņ &BDI XPSE JT BTTJHOFE POF PG UIF GPMMPXJOH UBHT BT TFFO JO 5BCMF  5BCMF  #*0 UBHT GPS OBNFE FOUJUZ SFDPHOJUJPO Tag Meaning O Not part of a named entity B-PER I-PER First word of a person name Continuation of a person name B-LOC First word of a location name ظ଴ग़ྗ #*0λά෇͚ •IOB (or IOB1): CoNLL 2003 データセットで使われている • 接頭辞 I を付与; NEが連続する場合,2番目以降 NE 開始単語に I-PER E-PER S-PER O S-LOC O B-PER I-PER I-PER B-PER O I-LOC O B-PER I-PER I-PER B-PER O I-LOC O I-PER IOB BIO BIOES 系列タグスキーマ https://speakerdeck.com/himkt/neural-named-entity-recognition #-0$
  12. จ຺ʹຒΊࠐ·Εͨ୯ޠͱݴޠֶతૉੑɿલஔࢺҙຯᐆດੑղফ !12  લஔࢺҙຯᐆດੑղফ QSFQPTJUJPOTFOTFEJTBNCJHVBUJPO  ‣ <B>໨త 163104& <C>डӹऀ

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  13. จ຺ʹຒΊࠐ·Εͨ୯ޠͱݴޠֶతૉੑɿલஔࢺҙຯᐆດੑղফ !13  લஔࢺҙຯᐆດੑղফ QSFQPTJUJPOTFOTFEJTBNCJHVBUJPO  ‣ <B>໨త 163104& <C>डӹऀ

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