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。

Transcript

1. !LNPUPIBT
   ຊڷݴޠॲཧษڧձ
   ୈ7ষ ࣄྫݚڀɿࣗવݴޠॲཧʹ͓͚Δૉੑ
   

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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
   

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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
   

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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
   

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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
   

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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/
   

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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
   

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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/
   

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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
   

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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
    

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11. จ຺ʹຒΊࠐ·Εͨ୯ޠɿݻ༗දݱೝࣝ
    !11
    ܥྻϥϕϦϯά໰୊ TFRVFODFMBCFMJOHUBTL
    ͱͯ͠ఆࣜԽ
    ‣ ϥϕϧ෇͚ͷεΩʔϚ#*0λά #FHJO*OTJEF0VUTJEFFODPEFEUBH
    
    $"4& 456%*&4 0' /-1 '&"563&4
    ɩF TFOUFODF BCPWF XPVME CF UBHHFE BT
    CQ?Mf"@S1_ aKBi?fA@S1_ -fP T`2bB/2MifP Q7fP J+*Q`KBFf"@P_: AM/mbi`B2bfA@P_:
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    ɩF USBOTMBUJPO GSPN OPOPWFSMBQQJOH TFHNFOUT UP #*0 UBHT BOE CBDL JT TUSBJHIUGPSXBSE
    -JLF 104UBHHJOH UIF /&3 UBTL JT B TUSVDUVSFE POF BT UBHHJOH EFDJTJPOT GPS EJĊFSFOU
    XPSET JOUFSBDU XJUI FBDI PUIFS JU JT NPSF MJLFMZ UP SFNBJO XJUIJO UIF TBNF FOUJUZ UZQF UIBO UP
    TXJUDI JU JT NPSF MJLFMZ UP UBH i+PIO 4NJUI *ODw BT "@P_: A@P_: A@P_: UIBO BT "@S1_ A@S1_
    "@P_:
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    DMBTTJmDBUJPO EFDJTJPOT
    ɩF DPSF GFBUVSF TFU GPS UIF /&3 UBTL JT TJNJMBS UP UIBU PG UIF 104UBHHJOH UBTL BOE SFMJFT
    PO XPSET XJUIJO B XPSET XJOEPX UP FBDI TJEF PG UIF GPDVT XPSE *O BEEJUJPO UP UIF GFBUVSFT PG
    FOUJUJFT TVDI BT .JMBO +PIO 4NJUI .D$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$
    

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12. จ຺ʹຒΊࠐ·Εͨ୯ޠͱݴޠֶతૉੑɿલஔࢺҙຯᐆດੑղফ
    !12
    લஔࢺҙຯᐆດੑղফ QSFQPTJUJPOTFOTFEJTBNCJHVBUJPO
    
    ‣ ໨త 163104&
    डӹऀ #&/&'*$*"3:
    ظؒ %63"5*0/
    ৔ॴ -0$"5*0/
    
    ‣ લஔࢺपΓͷ૭Λૉੑͱͯ͠༻͍Δͷ͸ཧ૝తͰ͸ͳ͍
    ‣ Ή͠ΖώϡʔϦεςΟοΫʢࠨଆͷ࠷ॳͷಈࢺˍӈଆͷ࠷ॳͷ໊ࢺʣ͕༗ޮ
    w લஔࢺͷࢧ഑ཁૉ HPWFSOPS
    ͱ໨తޠ PCKFDU
    Λ֫ಘͰ͖Δ
    803% */ $0/5&95 -*/(6*45*$ '&"563&4
    13&104*5*0/ 4&/4& %*4".#*(6"5*0/
    1SFQPTJUJPOT XPSET MJLF PO JO XJUI BOE GPS TFSWF GPS DPOOFDUJOH QSFEJDBUFT XJUI UIFJS BSHV
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    BNCJHVPVT $POTJEFS GPS FYBNQMF UIF XPSE GPS JO UIF GPMMPXJOH TFOUFODFT
    
    B 8F XFOU UIFSF GPS MVODI
    C )F QBJE GPS NF
    D 8F BUF GPS UXP IPVST
    E )F XPVME IBWF MFGU GPS IPNF CVU JU TUBSUFE SBJOJOH
    ɩF XPSE GPS QMBZT B EJĊFSFOU SPMF JO FBDI PG UIFN JO B
    JU JOEJDBUFT B 1ĦģġĠĤĖ JO C
    B #ĖğĖėĚ
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    B %ĦģĒĥĚĠğ BOE JO E
    B -ĠĔĒĥĚĠğ
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    DPSSFDU TFOTFT PG UIF QSFQPTJUJPOT XJUIJO JU ɩF QSFQPTJUJPOTFOTF EJTBNCJHVBUJPO UBTL EFBMT XJUI
    BTTJHOJOH UIF DPSSFDU TFOTF UP B QSFQPTJUJPO JO DPOUFYU GSPN B mOJUF JOWFOUPSZ PG TFOTFT 4DIOFJEFS
    FU BM < > EJTDVTT UIF UBTL QSFTFOU B VOJmFE TFOTF JOWFOUPSZ UIBU DPWFST NBOZ QSFQPTJ
    803%*/$0/5&95 -*/(6*45*$'&"563&413&104*5*0/4&/4&%*4".#*(6"5*0/
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    8IJDI BSF B HPPE TFU PG GFBUVSFT GPS UIF QSFQPTJUJPO TFOTF EJTBNCJHVBUJPO UBTL 8F GPMMPX
    IFSF UIF GFBUVSF TFU JOTQJSFE CZ UIF XPSL PG )PWZ FU BM <>
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    #FTJEFT UIBU
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    TFOUFODFT
    
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    hTBWFE GSPN IJNi XIJDI JOEFFE DPOUBJO UIF FTTFODF PG UIF QSFQPTJUJPO TFOTF *O MJOHVJTUJD UFSNT
    XF TBZ UIBU UIJT IFVSJTUJD IFMQT VT DBQUVSF UIF HPWFSOPS BOE UIF BOE PCKFDU PG UIF QSFQPTJUJPO
    #Z LOPXJOH UIF JEFOUJGZ PG UIF QSFQPTJUJPO BT XFMM BT JUT HPWFSOPS BOE PCKFDUT IVNBOT DBO JO
    

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13. จ຺ʹຒΊࠐ·Εͨ୯ޠͱݴޠֶతૉੑɿલஔࢺҙຯᐆດੑղফ
    !13
    લஔࢺҙຯᐆດੑղফ QSFQPTJUJPOTFOTFEJTBNCJHVBUJPO
    
    ‣ ໨త 163104&
    डӹऀ #&/&'*$*"3:
    ظؒ %63"5*0/
    ৔ॴ -0$"5*0/
    
    ‣ લஔࢺपΓͷ૭Λૉੑͱͯ͠༻͍Δͷ͸ཧ૝తͰ͸ͳ͍
    ‣ Ή͠ΖώϡʔϦεςΟοΫʢࠨଆͷ࠷ॳͷಈࢺˍӈଆͷ࠷ॳͷ໊ࢺʣ͕༗ޮ
    w લஔࢺͷࢧ഑ཁૉ HPWFSOPS
    ͱ໨తޠ PCKFDU
    Λ֫ಘͰ͖Δ
    w ґଘߏ଄ύʔβʢޙड़ʣͷ݁Ռ΋༻͍ͯϩόετʹ͢Δ
    803% */ $0/5&95 -*/(6*45*$ '&"563&4
    13&104*5*0/ 4&/4& %*4".#*(6"5*0/
    1SFQPTJUJPOT XPSET MJLF PO JO XJUI BOE GPS TFSWF GPS DPOOFDUJOH QSFEJDBUFT XJUI UIFJS BSHV
    NFOUT BOE OPVOT XJUI UIFJS QSFQPTJUJPOBM NPEJmFST 1SFQPTJPOT BSF WFSZ DPNNPO BOE BMTP WFSZ
    BNCJHVPVT $POTJEFS GPS FYBNQMF UIF XPSE GPS JO UIF GPMMPXJOH TFOUFODFT
    
    B 8F XFOU UIFSF GPS MVODI
    C )F QBJE GPS NF
    D 8F BUF GPS UXP IPVST
    E )F XPVME IBWF MFGU GPS IPNF CVU JU TUBSUFE SBJOJOH
    ɩF XPSE GPS QMBZT B EJĊFSFOU SPMF JO FBDI PG UIFN JO B
    JU JOEJDBUFT B 1ĦģġĠĤĖ JO C
    B #ĖğĖėĚ
    ĔĚĒģĪ JO D
    B %ĦģĒĥĚĠğ BOE JO E
    B -ĠĔĒĥĚĠğ
    *O PSEFS UP GVMMZ VOEFSTUBOE UIF NFBOJOH PG B TFOUFODF POF TIPVME BSHVBCMMZ LOPX UIF
    DPSSFDU TFOTFT PG UIF QSFQPTJUJPOT XJUIJO JU ɩF QSFQPTJUJPOTFOTF EJTBNCJHVBUJPO UBTL EFBMT XJUI
    BTTJHOJOH UIF DPSSFDU TFOTF UP B QSFQPTJUJPO JO DPOUFYU GSPN B mOJUF JOWFOUPSZ PG TFOTFT 4DIOFJEFS
    FU BM < > EJTDVTT UIF UBTL QSFTFOU B VOJmFE TFOTF JOWFOUPSZ UIBU DPWFST NBOZ QSFQPTJ
    803%*/$0/5&95 -*/(6*45*$'&"563&413&104*5*0/4&/4&%*4".#*(6"5*0/
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14. จ຺ʹຒΊࠐ·Εͨ୯ޠͷؒͷؔ܎ɿΞʔΫΛ୯Ґͱͨ͠ύʔδϯά
    !14
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15. จ຺ʹຒΊࠐ·Εͨ୯ޠͷؒͷؔ܎ɿΞʔΫΛ୯Ґͱͨ͠ύʔδϯά
    !15
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    ‣ ୯ޠΫϥελɾ୯ޠຒΊࠐΈͳͲͷ෼෍࿦త৘ใ
    w Ұൠʹґଘߏ଄໦ͷ܇࿅ίʔύε͸͋·Γେ͖͘ͳ͍
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16. ໨࣍
    !16
    ୈ̎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
    

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