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ACL2020 Category Survey: Sentiment Analysis
ACL2020 分野サーベイLT会の資料です。
https://nlpaper-challenge.connpass.com/event/191318/
ACL2020 に採択された sentiment analysis 系論文を読み、傾向をまとめ、気になった論文を詳しく紹介しています。
uchi_k
October 18, 2020
Transcript
45 slides
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ڮ ݎࢤ uchi_k @__uchi_k__ About me yuni, inc. ද ʮσʔλυϦϒϯͳͷͮ͘ΓΛࢧԉ͢ΔʯΛܝ͛ɺ
UGCղੳϚʔέςΟϯάσʔλղੳͷडୗɺࣗࣾ SleepTechϒϥϯυɺύʔιφϥΠζϚοτϨεͷ xSleepΛӡӦ͍ͯ͠·͢ɻ former ژେใӃ, ະ౿2016, ϑϦʔϥϯε, FreakOut Machine Learning Engineer -
Sentiment Analysis ͦͷଞ ൺֱબྨ ཁ આಘྗղੳ
ελϯεݕग़ લॲཧ ΞεϖΫτࣝผ ײݪҼϖΞநग़ ΞεϖΫτϕʔεײྨ ϨϏϡʔίϝϯτͷ VTFSHFOFSBUFE DPOUFOUT 6($ ͷϚΠ χϯά͕ଟ͘ɺ࢈ۀԠ༻ ࢤͷݚڀ͕ଟ͍ 6($ͷղੳͷΧς ΰϦ༻ঢ়گ͋Γ͖ͷ ײೝࣝʹͳΔ͜ͱ͕ଟ ͍ˠΞεϖΫτϕʔεײ ྨͷจ͕ଟ͍ -
$SPTT-JOHVBM6OTVQFSWJTFE4FOUJNFOU$MBTTJpDBUJPO XJUI.VMUJ7JFX5SBOTGFS-FBSOJOH #σʔλ͕গͳ͍ͷରॲྫɿผݴޠϦιʔεͷར༻ )POHMJBOH'FJ #BJEV3FTFBSDI 1JOH-J #BJEV3FTFBSDI "$- ࢿݯͷগͳ͍ݴޠʹ͍ͭͯɺڭࢣͳ͠ػց༁ͱݴޠผثΛͬͨ ڭࢣͳ͠ݴޠԣஅηϯνϝϯτྨϞσϧΛఏҊɻ
༁ثͱݴޠผثʹΑΔݴޠ BEWFSTBSJBMͳֶशͰݴޠීว ͳಛۭؒΛֶश͢Δ λʔήοτݴޠͷϥϕϧ͖Ϧ ιʔειʔεݴޠͱͷΫϩε ϦϯΨϧιʔεඞཁͱ͠ͳ͍ ͕৽͍͠ -
Sentiment analysis ݸผλεΫͷͭͳ͗߹Θͤʹͳ͍ͬͯΔɺͱ͍͏ͷɺྫ͑Ξε ϖΫτϕʔεηϯνϝϯτੳͰͷΞεϖΫτ༻ޠநग़ɺҙݟ༻ޠந ग़ɺηϯνϝϯτੳͷஈ֊ʹͳ͍ͬͯΔɺͳͲ λεΫͷचͭͳ͗λεΫ͝ͱͷݟ͕ڞ༗͞Εͳ͍ɺ૬ޓ࡞༻Λ͏ ·ֶ͘शͰ͖ͳ͍ͳͲͷ͕͋ΓɺUSBOTGPSNFSͳͲΛͬͨ FOEUPFOEͳख๏HSBQIBUUFOUJPOͳͲͷσʔλߏͰରॲ͢ ΔͳͲͷख๏͕ݟΒΕͨ ৽͍͠ͷରॲͱͯ͠ɺ4/4ͰݟΒΕΔΤίʔνΣΠϯόʔ
ݱʹΑΓରҙݟʹΞΫηε͢Δͷ͕͘͠ͳ͍ͬͯΔݱঢ়ʹରॲ͢ ΔͨΊɺٞʹର྆͠ۃੑͷҙݟΛநग़͢ΔɺͰ͋ͬͨΓɺTFYJTNత ͳߘͷରλΠϓʢܦݧஊͳͷ͔ྫ͑ͳͷ͔ʣ·Ͱਪఆ͢Δͷ ͳͲ -
"$PNQSFIFOTJWF"OBMZTJTPG1SFQSPDFTTJOH GPS8PSE3FQSFTFOUBUJPO-FBSOJOHJO"⒎FDUJWF5BTLT #abstract /BTUBSBO#BCBOFKBE %FQBSUNFOUPG&MFDUSJDBM&OHJOFFSJOHBOE$PNQVUFS4DJFODF FUBM "$- ಛʹײೝࣝܥͷλεΫʹ͓͍ͯલॲཧ͕୯ޠຒΊࠐΈʹ༩͑ΔӨڹΛௐɺ Α͘ߦΘΕΔ࣮ݧઃఆ͕ຊʹਖ਼͍͠ͷ͔ݕূ͢Δ ֶशࡁΈͷ୯ޠຒΊࠐΈΛ͍͕͚ͪͩͲɺྫ͑ʮͤʯͱʮ൵͠Έʯͷ
ϖΞ͕ʮͤʯͱʮتͼʯͷϖΞΑΓྨࣅ͕ߴ͘ͳΔΑ͏ͳຒΊࠐΈ͕ଘ ࡏ͢Δͷʹײೝ͕ࣝຊʹղ͚Δʁ 4UPQXPSET OFHBUJPO 104 MFNNBUJ[BUJPOͳͲͷલॲཧΛͲ͏͏͔ ͕ຊ࣭తʹॏཁͳͷͰʁ લॲཧ͕୯ޠຒΊࠐΈʹ༩͑ΔӨڹͷେ͖͞Λݕূ͠ɺैདྷͷ࣮ݧઃఆͷݟ ͠Λߦ͍͍ͨ -
#distributional hypothesis #word embedding Ծઆʹجͮ͘୯ޠຒΊࠐΈͷݶք ʮͤʯͱʮ൵͠ΈʯͷϖΞ͕ʮͤʯͱʮتͼʯͷϖΞΑΓྨࣅ ͕ߴ͘ͳΔɺͳͲײʹ͢Δྨࣅ͕ಘΒΕΔ͜ͱ͋ΓɺλεΫ ͝ͱʹ୯ޠຒΊࠐΈΛௐ͢Δඞཁ͕͋Δ The Distributional
Hypothesis is that words that occur in the same contexts tends to have similar meanings [Harris, 1954]. ࣅͨจ຺Ͱසൟʹग़ݱ͢Δ୯ޠಉ࢜ҙຯతʹྨࣅ͍ͯ͠Δͱߟ͑ͯɺ ຒΊࠐΈۭؒͰۙ͘ͳΔͱ͍͏Ծઆ ୯ޠͷҙຯΛܾΊΔͨΊͷҰͭͷํ๏ͱͯ͠ɺԾઆ͕͋Δɻ ౷ܭతʹ୯ޠͷҙຯΛಘΔͨΊͷํ๏ͰɺXPSEWFDͷΑ͏ͳਪ ϕʔεͷϞσϧ୯ʹ౷ܭใΛ࣍ݩݮ͢ΔΧϯτϕʔεͷख๏ ͋Δ -
"$PNQSFIFOTJWF"OBMZTJTPG1SFQSPDFTTJOH GPS8PSE3FQSFTFOUBUJPO-FBSOJOHJO"⒎FDUJWF5BTLT #abstract λεΫݻ༗ͷඍௐϞσϧͷվળॏཁͰ͋Δ͕ɺઌߦݚڀ͔Βલॲ ཧϋΠύʔύϥϝʔλͷӨڹ͕ແࢹͰ͖ͳ͍͜ͱ͕ಡΈऔΕΔ ֶश༻σʔλͷ୯ޠຒΊࠐΈΛߦ͏લɾޙͦΕͧΕͷλΠϛϯάͰલॲཧΛ ߦͬͨΓɺςετσʔλͷલॲཧͱ߹ΘͤͨΓ߹Θͤͳ͔ͬͨΓΛࢼ͢ /BTUBSBO#BCBOFKBE %FQBSUNFOUPG&MFDUSJDBM&OHJOFFSJOHBOE$PNQVUFS4DJFODF FUBM
"$- ಛʹײೝࣝܥͷλεΫʹ͓͍ͯલॲཧ͕୯ޠຒΊࠐΈʹ༩͑ΔӨڹΛௐɺ Α͘ߦΘΕΔ࣮ݧઃఆ͕ຊʹਖ਼͍͠ͷ͔ݕূ͢Δ -
ؔ࿈ݚڀʢTFOUJNFOU FNPUJPOʣ • &NPUJPO$BVTF1BJS&YUSBDUJPO"/FX5BTLUP&NPUJPO "OBMZTJTJO5FYUT ◦ 3VJ9JB 4DIPPMPG$PNQVUFS4DJFODFBOE&OHJOFFSJOH FUBM "$-
◦ FNPUJPOͱDBVTFͷϖΞΛநग़͢Δ৽͍͠λεΫͷఏҊɻFNPUJPOͱ DBVTFͷϖΞͰϚϧνλεΫֶशΛߦ͏ • /-'**5BU*&45&NPUJPO3FDPHOJUJPOVUJMJ[JOH/FVSBM /FUXPSLTBOE.VMUJMFWFM1SFQSPDFTTJOH ◦ 4BNVFM1FDBS 4MPWBL6OJWFSTJUZPG5FDIOPMPHZ FUBM &./-1 ◦ 6TFSHFOFSBUFEDPOUFOUTΛ༻͢Δ߹ͷલॲཧͷॏཁੑʹ͍ͭͯௐ ͍ͯΔɻಛʹإจࣈֆจࣈͷೝࣝΛৄ͘͠ߦ͍είΞΛ্͛Δ͜ͱʹޭ -
ؔ࿈ݚڀʢXPSEWFD 6($ʣ • *NQSPWJOH%JTUSJCVUJPOBM4JNJMBSJUZXJUI-FTTPOTGSPN8PSE &NCFEEJOHT ◦ 0NFS-FWZ #BS*MBO6OJWFSTJUZ FUBM "$-
◦ 8PSEFNCFEEJOHʹ͓͍ͯɺΧϯτϕʔεͷख๏ͰϋΠύʔύϥϝʔ λௐ࣍ୈͰXPSEWFDͳͲͷਪϕʔεͷख๏ʹউͯΔ͜ͱΛࣔͨ͠ ◦ ख๏ॏཁ͕ͩɺϋΠύʔύϥϝʔλͷٞॏཁͱ͍͏͜ͱΛఏى • /-'**5BU*&45&NPUJPO3FDPHOJUJPOVUJMJ[JOH/FVSBM /FUXPSLTBOE.VMUJMFWFM1SFQSPDFTTJOH ◦ 4BNVFM1FDBS 4MPWBL6OJWFSTJUZPG5FDIOPMPHZ FUBM &./-1 ◦ 6TFSHFOFSBUFEDPOUFOUTΛ༻͢Δ߹ͷલॲཧͷॏཁੑʹ͍ͭͯௐ ͍ͯΔɻಛʹإจࣈֆจࣈͷೝࣝΛৄ͘͠ߦ͍είΞΛ্͛Δ͜ͱʹޭ ◦ લॲཧʹΧςΰϥΠζ͞ΕΔΑ͏ͳॲཧΛ͔ͬ͠ΓΔ͜ͱͰείΞ্ʹ ͭͳ͕Δͱ͍͏͜ͱ͕จͰࣔ͞Εͨ #recent study #ugc #word2vec -
ؔ࿈ݚڀʢલॲཧʣ • 0OTUPQXPSET pMUFSJOHBOEEBUBTQBSTJUZGPSTFOUJNFOU BOBMZTJTPGUXJUUFS ◦ )BTTBO4BJG 5IF0QFO6OJWFSTJUZ FUBM -3&$
◦ ετοϓϫʔυͷআڈ͕༗ޮ͔ͦ͏Ͱͳ͍͔ϫʔυϦετͷ࡞Γํλε ΫͰେ͖͘ҟͳΔ͕ɺUXJUUFSTFOUJNFOUͰҰൠతͳํ๏ͩͱͷํ͕େ ͖͍͜ͱΛࣔͨ͠ ◦ Ұൠతͳલॲཧख๏ΛφΠʔϒʹద༻͢Δ͚ͩͰ͍͚ͳ͍͜ͱ͕͋Δ͜ͱ Λࣔͨ͠ • "DPNQBSBUJWFFWBMVBUJPOPGQSFQSPDFTTJOHUFDIOJRVFTBOE UIFJSJOUFSBDUJPOTGPSUXJUUFSTFOUJNFOUBOBMZTJT ◦ 4ZNFPO4ZNFPOJEJT &YQFSU4ZTUFNTXJUI"QQMJDBUJPOT ◦ લॲཧͷςΫχοΫΛ৭ʑࢼͯ͠ΈͨΒɺײੳͰMFNNBUJ[BUJPOͱ ࣈͷআڈɺॖܗͷஔ͕࠷είΞʹد༩ ◦ ྨσʔλͷલॲཧʹؔͯ͠แׅతͳείΞධՁΛߦͬͨ #recent study #preprocessing #emotion -
#preprocessing #pipeline /-1ʹ͓͚ΔલॲཧͷྲྀΕ ΫϦʔχϯά ׂ ਖ਼نԽ ѹॖ ϕΫτϧԽ λά ه߸ͳͲͷআڈ
QVODUVBUJPO ܗଶૉղੳ ࣙॻͷՃ Γड͚ղੳ ࣈͷஔ͖͑ إจࣈͳͲͷೝࣝ TQFMMDIFDL දهΏΕ MPXFSDBTJOH දޠͷஔ͖͑ লུޠ MFNNBUJ[BUJPO TUFNNJOH OFHBUJPO Φϯτϩδʔ 4UPQXPSEͷআڈ 104 $#08 TLJQHSBN #&35 DPWFSBHFͷௐࠪ ྨσʔλͱޠኮΛ͚ۙͮΔ FUD -
#preprocessing #negation /FHBUJPO • ҙޠࣙॻͷ࡞ ◦8PSE/FUίʔύεͰҙޠࣙॻΛ࡞ ◦ҙޠ͕ݟ͔ͭΒͳ͍PSͭͰ͋Εͦͷ··ɺෳ͋Δ߹ VL8BDίʔύεͷதͰ࠷େͷසΛ࣋ͭҙޠͱͨ͠Γ୯ʹϥϯμϜ ʹબͨ͠Γ •
൱ఆޠͷҙޠͷஔ ◦൱ఆޠ͕ݟ͔ͭͬͨ߹ɺଓ͘୯ޠΛநग़͠ɺҙޠࣙॻͰҙޠΛ ݕࡧɻҙޠ͕ݟ͔ͭͬͨ߹ɺ൱ఆޠͱ൱ఆ͞ΕͨޠΛͦΕʹஔ͖ ͑Δ ◦ྫ͑ɺ<b* BN OPU IBQQZ bUPEBZ`>ͱ͍͏จͰɺ൱ఆޠʢ`OPUʣ ͱͦΕʹରԠ͢Δ୯ޠʢIBQQZʣΛಛఆɻҙޠࣙॻͰbIBQQZ`ͷ ҙޠʢ`TBE`ʣΛ୳͠ɺOPUIBQQZ`ΛbTBE`ʹஔ͖͑Δ -
#corpus #training #dataset /FXT શମͱͯ͠ɺ4UPQXPSEͷআڈ104ͰWPDBCTJ[F͋·Γม ΘΒͳ͍͕DPSQVTTJ[F͕େ͖͘ݮগ ʙͷΞϝϦΧͷͷग़ ൛͔Βͷ ݅ͷهࣄ 8JLJQFEJB
8JLJQFEJBͷهࣄ ݅Ͱ ߏ͞ΕΔɺ/FXTΑΓഒେ͖ ͍ίʔύε 5SBJOJOH$PSQVT ͭͷαΠζɾੑ࣭ͷҟͳΔίʔύεʹͭͷલॲཧΛߦ͏ -
#corpus #evaluation #dataset &WBMVBUJOH$PSQVT 4FOUJNFOUBOBMZTJT FNPUJPODMBTTJpDBUJPO TBSDBTNEFUFDUJPOͷͭͷλεΫͰධՁɻ • *.%#
◦ ݅ͷөըϨϏϡʔɻϙδωΨൺ • 4FN&WBM ◦ πΠʔτɻϙδωΨൺ • "JSMJOF ◦ ߤۭձࣾࣾʹؔ͢Δ݅πΠʔτɻ 4FOUJNFOUBOBMZTJTײϙδωΨ • *4&"3 ◦ ݅ͷɺײΛשى͢Δݸਓతͳ • "MN ◦ ݅ͷ͓ͱ͗ • 44&$ ◦ 4FN&WBMΛ࠶Ξϊςʔγϣϯͨ݅͠ͷπ Πʔτ &NPUJPO%FUFDUJPOײΫϥεྨ 4BSDBTN%FUFDUJPOൽͷݕग़ • 0OJPO ◦ ൽΛѻ͏ϝσΟΞͱͦ͏Ͱͳ͍ϝσΟΞ͔Βऩू ͨ݅͠ͷχϡʔεϔουϥΠϯ • *"$ ◦ ݅ͷൃԠ • 3FEEJU ◦ ஶऀ͕ϥϕϧ͚ͨ͠ສ݅ͷ3FEEJUߘ -
#result #compare with SoTA 4P5"ϕʔεϥΠϯʹର͢ΔఏҊϞσϧͷධՁ શͯͷλεΫͰఏҊख๏܈͕4P5"Λ্ճΔ #&35͕Ұ൪ڧ͍ͷવͳͷͰগͣ͠Δ͍͕ɺఏҊख๏શମͱͯ͠উͬͯ ͍Δͷ͕ଟ͍ʢ*.%# *"$ 0OJPO
3FEEJU 44&$ʣ 4P5"ͳࣄલֶशϞσϧఏҊख๏ΑΓང͔ʹେ͖͍ίʔύεΛ͍ͬͯΔ ͷͰɺQSFͷॏཁੑ͕Θ͔Δ -
#key points ·ͱΊ ୯ޠຒΊࠐΈ࣌Ͱͷલॲཧ͕࠷λΠϛϯάͱͯ͠༗ޮͰ͋Δ͜ͱ ͕ࣔ͞Εͨ ୯ମͱͯ͠OFHBUJPO͕࠷ޮՌ͕͋ΓɺҰൠతͳTUPQXPSET TUFNNJOHείΞΛԼ͛Δ͜ͱ͕ଟ͍ ڊେͳίʔύεͰֶशࡁΈͷ୯ޠຒΊࠐΈΛ͏ΑΓɺλεΫʹ߹Θ ͤͨલॲཧΛదͳλΠϛϯάͰߦ͏͜ͱͰείΞͰ্ճΕΔ ҉తʹΒΕ͍ͯͨݟ͕ଟ͍͕ɺ͔ͬ͠Γͨ͠ݕূΛߦ͏͜ͱ
Ͱମܥతͳࣝʹͨ͠ ਖ਼·ͩ·࣮ͩݧ͕Γ͍ͯͳ͍෦͋Δͱײ͕ͨ͡ɺΑΓแׅత ͳ࣮ݧΛ͢Δ͜ͱͰ͔ͳΓ໘ന͘ͳΓͦ͏ -
)FUFSPHFOFPVT(SBQI/FVSBM/FUXPSLT GPS&YUSBDUJWF%PDVNFOU4VNNBSJ[BUJPO #abstract จॻཁͰɺηϯςϯεؒͷؔੑͷϞσϧԽ͕ ඇৗʹॏཁɻैདྷɺ3//ϕʔεͷख๏ͰܥྻͰ ϞσϧԽ͍ͯͨ͠ %BORJOH8BOH 4IBOHIBJ,FZ-BCPSBUPSZPG*OUFMMJHFOU*OGPSNBUJPO1SPDFTTJOH 'VEBO6OJWFSTJUZ FUBM
"$- நग़తจॻཁͰηϯςϯεؒͷؔੑΛදݱ͢ΔͨΊʹ IFUFSPHFOFPVTHSBQIΛಋೖ͠ɺ4P5"Λୡ֦ுੑͳͲʹ͍ͭͯݕূͨ͠ɻ จॻͷҙຯߏܥྻΑΓάϥϑߏͷํ͕దͯ͠ ͍Δ͜ͱ͕࠷ۙͷݚڀͰΘ͔͖͍ͬͯͯΔ͕ɺྑ͍ άϥϑߏ·ͩఏҊ͞Ε͍ͯͳ͔ͬͨ ୯ޠϊʔυͱจϊʔυΛ࣋ͭIFUFSPͳHSBQIߏ ΛఏҊ͠ɺ୯จॻɾଟจॻཁͦΕͧΕͰ 4P5"Λୡɻ֦ுੑʹ͍ͭͯٞͨ͠ -
#abstract #extractive document summarization ݩͷจॻ͔Βؔ࿈͢ΔจॻΛऔΓग़ͯ͠ɺཁ ͱͯ͠࠶ߏ͢ΔλεΫ நग़తจॻཁ ୯ޠΛܦ༝ͨ͠จͷؔੑΛදݱ͢ΔIFUFSPHSBQIΛఆٛ υΩϡϝϯτͷ֤ηϯςϯεΛ#JEJSFDUJPOBM-45.ͰϕΫτϧԽɻ͜Ε ʹΑͬͯηϯςϯεͷҙຯΛଊ͑ͨϕΫτϧ͕࡞ΒΕΔʢXPSEMBZFSʣ
நग़ܕͱɺදݱΛநԽͯ͠θϩ͔ΒཁจΛ ࡞ΔੜܕɺͦΕΒͷࠞ߹ͷύλʔϯ͕͋Δ ͞Βʹ͜ͷϕΫτϧಉ࢜ͷؔੑΛ#JEJSFDUJPOBM-45.Ͱֶश͢Δ ʢTFOUFODFMBZFSʣ ηϯςϯεΛநग़͢Δ֬Λग़ྗ 4VNNB3V//FS ॳظͷݚڀ -
#model overview ηϯςϯεͷΈΛϊʔυͱͯ͠άϥϑΛߏங͢ ΔͷͰͳ͘ɺηϯςϯεΛͭͳ͙հͷΑ ͏ͳϊʔυΛՃ 1SPQPTFE(SBQI ୯ޠΛܦ༝ͨ͠จͷؔੑΛදݱ͢ΔIFUFSPHSBQIΛఆٛ จใͰ୯ޠϊʔυΛߋ৽Ͱ͖Δ ଞͷϊʔυλ ΠϓΛՃ͢ΔͳͲͷ֦ுੑ͕͋ΔɺͳͲͷར
͜ͷจͰɺ࠷খҙຯ୯ҐΛ୯ޠʹ͍ͯ͠ Δɻྫ͑ɺΑΓநԽͯ͠୯ޠͷҙຯ֓೦ ΛϊʔυλΠϓͱ͢Δ͜ͱ໘നͦ͏ HSBQIJOJUJBMJ[Fˠ("5Ͱߋ৽ˠηϯςϯε ಛ͔ΒཁจʹՃ͢Δ͔൱͔ͷྨΛ ղ͘ɺͱ͍͏खॱ -
#model overview #learning step HSBQIJOJUJBMJ[FSͰɺจʹΧʔωϧαΠζͷҟ ͳΔ$//Λద༻ͯ͠OHSBNಛΛநग़ʢہ ॴಛʣɺ࣍ʹ#J-45.ͰηϯςϯεϨϕϧͷ ಛΛநग़ʢେҬಛʣ 1SPQPTFE(SBQI ֶशखॱͱNPEFMPWFSWJFX
୯ޠϊʔυͱจϊʔυͷؔੑʹؔ͢Δใͱ ͯ͠ɺUGJEGΛΤοδಛͰ༻͢Δ άϥϑಛ(SBQI"UUFOUJPO/FUXPSLͰ ߋ৽ -
#model overview #graph attention network ࣗͱपғʹͦΕͧΕॏΈΛ͔͚ͨϕΫτϧ͔ΒBUUFOUJPOΛܭࢉ ͠ɺपลϊʔυ͔ΒͷBHHSFHBUJPOʹར༻ (SBQI"UUFOUJPO/FUXPSL άϥϑ্ͰͷBUUFOUJPOΛఆٛ "UUFOUJPO
ྡϊʔυ "UUFOUJPOΛܭࢉ͢Δؔ "UUFOUJPOΛߟྀͨ͠ BHHSFHBUJPO άϥϑूͷڑؔΛɺάϥϑߏʹґଘ͠ͳ͍BUUFOUJPOͱͯ͠ ఆֶٛ͠शϕʔεͰٻΊΔɺΈ͍ͨͳ ϊʔυಛ -
#dataset #train test split %BUBTFU ୯จॻཁͰͭɺෳจॻཁͰͭͷσʔληοτͰ࣮ݧ • ୯จॻཁͰ࠷͘ར༻͞Ε͍ͯΔϕϯνϚʔΫσʔληοτ • USBJO
WBMJE UFTUσʔλͦΕͧΕ $//%BJMZ.BJM2"σʔλ • /FX:PSL5JNFT"OOPUBUFE$PSQVT 4BOEIBVT ͔Βऩू͞Εͨ୯จॻཁ σʔληοτ • USBJO WBMJE UFTUσʔλͦΕͧΕ ݅ /:5 .VMUJ/FXT • ෳจॻཁσʔληοτ • ͦΕͧΕʙͷจॻʹର͠ɺਓ͕ؒॻ͍ͨཁ͕͋Δ • USBJO WBMJE UFTUσʔλͦΕͧΕ -
#experiment #setting #hyper-parameter #preprocessing 4FUUJOH)ZQFSQBSBNFUFST લॲཧ άϥϑ ࣮ݧ ετοϓϫʔυ۟ಡͷআڈ ೖྗจॻͷ࠷େΛจʹ
ઃఆ UGJEGԼҐΛআڈ ޠኮΛʹ੍ݶ ࣍ݩͷ(MP7FͰຒΊࠐΈ จϕΫτϧαΠζͰॳظԽ Τοδಛྔ ࣍ݩͰॳظԽ IFBE όοναΠζ ֶशF "EBN FQPDIͰMPTT ͕Լ͕Βͳ͍߹FBSMZTUPQQJOH ୯จॻཁͰ্Ґจ ෳจॻཁͰ্ҐจΛબ -
#methods #extractor • &YU#J-45. ◦$// #J-45. ◦จॻΛจͷܥྻͱΈͳ͠จؔΛֶश͢Δ • &YU5SBOTGPSNFS ◦5SBOTGPSNFS
USBOTGPSNFS ◦શจͷϖΞϫΠζ૬ޓ࡞༻Λֶश ◦จϨϕϧͷશ࿈݁άϥϑͱΈͳͤΔ • )4( )FUFS4VN(SBQI ◦ఏҊख๏ɻจ୯ޠจͷؔੑΛάϥϑͰϞσϧԽ ◦)4(ͰϊʔυྨʹΑͬͯཁจΛબ͠ɺ͞ΒʹUSJHSBN CMPDLJOHʹΑͬͯUSJHSBN͕ࣅ͍ͯΔจΛআ֎͠ੑΛ͑ͨόʔ δϣϯ࣮ݧ .FUIPET -
#result #CNN/DailyMail 3FTVMUʢ୯จॻཁɿ$//%BJMZ.BJMʣ $//%BJMZ.BJMͰͷ୯จॻཁͷ݁Ռɻطଘख๏ͯ͢Λ্ճΔείΞ͕ಘΒΕͨɻ -&"%͕ϕʔεϥΠϯɺ 03"$-&͕VQQFSCPVOE MBCFM QSFWJPVTTUVEZ QSPQPTFENFUIPE จ຺όϯσΟουͱͯ͠ఆٛ
ͨ͠)&3ʹؔͯ͠ಛʹϙϦ γʔ͋Γͳ࣮͠ݧ͠ɺ͍ͣΕ উͪ ʢ#&35Λ͍ͬͯͳ͍ʣશͯͷطଘख๏ΑΓߴ͍είΞ͕ಘΒΕͨ 306(& -ͰධՁɻͦΕ ͧΕHSBN HSBN Ұக͢Δ ࠷ܥྻͷྨࣅͷείΞ -
#result #CNN/DailyMail 3FTVMUʢ୯จॻཁɿ$//%BJMZ.BJMʣ จܥྻશଓάϥϑΛར༻ͨ͠ख๏ͱൺΔ͜ͱͰɺ IFUFSPHSBQIߏͷ༗༻ੑ͕ࣔ͞Εͨɻ &YUNFUIPE QSPQPTFENFUIPE จܥྻɺશଓάϥϑΛͬ ͨ&YU#J-45. &YU
5SBOTGPSNFSΑΓߴ͍είΞ IFUFSPHSBQIΛ͏͜ͱͰɺ ηϯςϯεؒͷෆཁͳ݁߹ΛޮՌ తʹআڈͰ͖͍ͯΔ -
#result #NYT50 3FTVMUʢ୯จॻཁɿ/:5ʣ /:5Ͱͷ୯จॻཁͷ࣮ݧ݁Ռɻ$//%BJMZ.BJMͱجຊతʹಉ͕͡ݟΒΕͨɻ جຊతʹ$//%BJMZ.BJM ͱಉ͡ͰɺఏҊख๏͕طଘ ख๏Λ্ճ͍ͬͯΔ QSPQPTFENFUIPE USJHSBNCMPDLJOH͋Γ όʔδϣϯ͕ҐͰͳ͍
ͷͳͥɾɾɾʁ ˠ$//%BJMZ.BJMͰॏෳͷ গͳ͍Օॻ͖Λ࿈݁͢Δܗࣜ ͕ͩɺ/:5ͰΩʔϑ Ϩʔζ͕ෳճొ͢ΔͳͲॏ ෳ͕͋ΔɻͳͷͰɺUSJHSBN CMPDLJOHͰ/:5Ͱε ίΞΛग़ͮ͠Β͍ͷͰ -
#ablation #CNN/DailyMail ୯ޠϑΟϧλϦϯάͷআͰ 3 3-είΞݮগ 3 είΞ૿Ճ "CMBUJPO $//%BJMZ.BJMͰBCMBUJPO͠ϞδϡʔϧͷߩݙΛௐͨɻ ୯ޠϑΟϧλϦϯάʹΑΓɺಛʹॏཁͳ୯ޠϊʔυʹϑΥʔΧεͰ͖Δར
͕CJHSBNใΛࣦ͏σϝϦοτΛ্ճ͍ͬͯΔͷͰͳ͍͔ ("5ؒͷSFTJEVBM DPOOFDUJPOΛআ͢Δ͜ͱͰ είΞ͕େ͖͘ݮগ ("5ͷSFTJEVBMDPOOFDUJPOɺIFUFSPHSBQIʹ͓͚ΔผλΠϓͷ ϊʔυ͔ΒͷूͰཧతʹॏཁͳͷͰ୯ͳΔ݁߹Ͱஔ͖͑Ͱ͖ͳ͍ -
#result #multidocument )4( )%4(ڞʹطଘख๏Λ্ճ ΔείΞ͕ಘΒΕ͍ͯͯɺಛʹ )%4(ͰείΞ্ঢ͕େ͖͍ 3FTVMUʢଟจॻཁʣ ଟจॻཁͰจॻϊʔυΛՃͨ͠ఏҊख๏Ͱݕূ จॻϊʔυͷՃ͕ଟจॻཁʹ ޮՌతͰ͋Δ͜ͱ͕ࣔࠦ
USJHSBNCMPDLJOH͕ޮ͍͍ͯͳ͍ ͷɺ͓ͦΒ͖ͬ͘͞ͱಉ͡ཧ༝ ఏҊख๏Ͱ୯ʹϊʔυλΠϓΛՃ͢Δ͚ͩͰผλεΫʹԠ༻Ͱ͖͓ͯ Γɺൃలੑ͕ߴ͍ QSPQPTFENFUIPE -
#qualitative analysis #degree ୯ޠϊʔυͷ͕ߴ͍ͱɺͦͷ୯ޠ ͷग़ݱ͕ଟ͍ͱ͍͏͜ͱʹͳΓจॻ ͷΛʢଟগʣද͢ 2VBMJUBUJWF"OBMZTJT ୯ޠϊʔυͷ͕༩͑ΔӨڹΛௐࠪ ୯ޠϊʔυ͕͋Δ͜ͱͰɺจใͷूͱେҬදݱͷ͕ߦΘΕ͍ͯΔՄ ೳੑ͕ࣔࠦ͞ΕΔ
୯ޠͷͱ306(&͕ൺྫ ˠੑͷߴ͍จॻ΄Ͳཁ͠қ͍ ͕ߴ͍ͱෳͷจͷใΛू͢ Δ͜ͱ͕Ͱ͖ɺϞσϧͷԸܙΛΑΓڧ ͘ड͚Δ͜ͱ͕Ͱ͖Δͱߟ͑ΒΕΔ -
#qualitative analysis #source จॻ͕૿Ճ͢Δ͜ͱͰɺϕʔεϥΠϯ ্ঢ͢Δ͕ఏҊख๏ͰԼ͠ จͰฒͿ 2VBMJUBUJWF"OBMZTJT ଟจॻཁͰɺจॻͷͷӨڹΛௐࠪ จॻͷ૿ՃͰ)&5&346.(3"1)ͱ)&5&3%0$46.(3"1)ͷੑ
ೳ͕֦ࠩେจॻͱจॻͷ͕ؔෳࡶʹͳΔ΄Ͳɺจॻϊʔυͷར͕Α Γେ͖͘ͳΔ 'JSTUɺΧόϨοδΛ֬อͰ͖Δ จষΛ֤จॻ͔Βڧ੍తʹநग़Ͱ͖Δ จॻͷ૿Ճʹ͍ɺશจͷओࢫΛΧ όʔͰ͖ΔݶΒΕͨͷจΛநग़͢Δ ͜ͱ͕ࠔʹͳ͍ͬͯͨ͘Ί -
#key points ·ͱΊ IFUFSPHSBQIΛ͏͜ͱͰɺจॻཁʹpOFHSBJOFEͳҙຯ୯Ґ Λಋೖ͢Δ͜ͱ͕Ͱ͖ɺจɾจষؒͷؔੑͷϞσϦϯάͷ༗ޮੑ ͕͔֬ΊΒΕͨ ख๏ͷ֦ுੑߴ͘ɺ୯จॻཁ͔ΒϊʔυλΠϓͷՃͷΈͰଟจ ॻཁʹରԠՄೳ IFUFSPHSBQIʹಛԽͨ͠ख๏ʢϝλύεΛͬͨαϒάϥϑͷఆ ٛɺIFUFSPHSBQIʹର͢ΔBUUFOUJPOʣΛࢼ͢ͱ໘ന͍͔
ࠓޙ#&35ࣄલֶशϞσϧΛ͍Ζ͍Ζݕ౼͍ͨ͠ͱͷ͜ͱ චऀܰ͘৮Ε͍͕ͯͨɺ୯ޠϊʔυʹͨΔ෦͕ҙຯϊʔυ·Ͱ நԽ͞ΕͨΓͨ͠Βख๏ͷ༏Ґੑ͕ΑΓ׆͔͞ΕΔͱࢥ͏ɻͦ͏Ͱ ͳͯ͘ɺϊʔυλΠϓͷՃ͍Ζ͍Ζࢼͤͦ͏