言語処理学会 第25回年次大会 発表論文集 (2019年3月)

υϝΠϯɾϢʔβʔࢦ޲ࣙॻͷର࿩తߏஙখൺా ྋհ ಹਢ઒ ఩࠸ ٢ా Ұ੕ ۚࢁ ത೔ຊΞΠɾϏʔɾΤϜ‫ࣜג‬ձࣾɹ౦‫ॴڀݚૅجژ‬kohi@ibm.com, {nasukawa, issei, hkana}@jp.ibm.com

1͸͡Ίʹͯ͞ɼީิͷબग़ʹؔͯ͠͸ɼʮ֤୯‫ޠ‬ͷಛ௃ྔϕςΩετϚΠχϯάͷ໨త͸ɼେྔͷੜจॻ͔Βҙຯͷ͋ΔύλʔϯΛൃ‫͠ݟ‬ɼͦͷύλʔϯͷഎ‫͋ʹޙ‬Δ༗ӹͳ஌‫ݟ‬ΛಘΔ͜ͱͰ͋Δ [5]ɽͦͯ͠ɼͦͷ෼ੳաఔʹ͓͍ͯෆՄܽͱͳΔͷ͕୯‫͋Ͱॻࣙޠ‬Δ [2]ɽྫ
͑͹ɼंʹؔ͢Δจॻ͔ΒʮͲͷंछ͕Ͳ͏͍ͬͨ‫ػ‬ೳΛ͍࣋ͬͯΔͷ͔ʯΛ஌Ζ͏ͱͨ࣌͠ʹɼʮंछʹؔ͢Δ୯‫ॻࣙޠ‬ʢྫ ϓϦ΢εɼϚʔνͳͲʣʯͱʮ‫ػ‬ೳʹؔ͢Δ୯‫ॻࣙޠ‬ʢྫ ࣗಈΞγετɼόοΫϞχλʔͳͲʣʯΛ༻ҙ͢Δ͜ͱʹΑͬͯɼंछͱ‫ػ‬ೳͷؔ܎ੑΛநग़Ͱ͖ΔɽຊߘͰ͸ɼ͜͏ͨ͠୯‫ॻࣙޠ‬Λɼ‫ػ‬ցʹΑΔީิఏࣔͱਓ͔ؒΒͷϑΟʔυόοΫʹΑͬͯޮ཰తʹߏங͢Δखஈʹ͍ͭͯఏҊ͢Δɽ্Ͱड़΂ͨ୯‫ॻࣙޠ‬͸ҎԼͷೋͭͷಛ௃Λ࣋ͭɽ(i) υϝΠϯʹґଘ͠ɼҰൠ஌ࣝͰ͸ෆे෼Ͱ͋Δɽ(ii) Ϣʔβʔͷ෼ੳಈ‫ʹػ‬Ԡͯ͡ɼҰͭͷ୯‫͕ޠ‬ҟͳΔࣙॻʹొ࿥͞Ε͏ΔɽΫτϧΛఆٛ͠ɼͦͷϕΫτϧۭؒͰͷྨࣅ౓͕ߴ͍΋ͷΛఏࣔ͢Δʯͱ͍͏ख๏͕ҰൠతͰ͋Γɼಛ௃ྔʹ͸ Word2Vec ͳͲपล୯‫ޠ‬෼෍ʹؔ͢Δ΋ͷ͕ѻΘΕɼࣅͨจ຺Λ࣋ͭ୯‫ͯ͠ͱิީ͕ޠ‬ఏࣔ͞ΕΔ [1, 3]ɽ
ಛ௃ྔΛ౰֘จॻ಺Ͱߏங͢Δ͜ͱʹΑͬͯɼ୯‫ޠ‬ͷυϝΠϯੑΛ൓ө͢Δ͜ͱ͸Ͱ͖Δ͕ɼ͔͠͠ɼϢʔβʔ͕‫ٻ‬Ί͍ͯΔࣙॻΛਪఆ͢Δʹ͸ෆे෼Ͱ͋ΓɼϑΟʔυόοΫΛީิબग़ʹ൓өͤ͞Δඞཁ͕͋ΔɽҎ্Λ౿·͑ɼຊߘͰ͸ର࿩‫ܕ‬ͷࣙॻߏஙࢧԉख๏ΛఏҊ͢ΔɽఏҊख๏͸είΞ‫ࢉܭ‬ʢ2.1 અʣͱॏΈֶ
शʢ2.2 અʣͷೋͭʹ෼͔ΕΔɽείΞ‫Ͱࢉܭ‬͸ɼҟͳΔ‫͔఺؍‬Β୯‫ޠ‬ͷྨࣅੑΛଊ͑ΔҟͳΔಛ௃ྔΛ࢖ͬͨϞσϧΛಠཱʹอ࣋͠ɼϞσϧ͝ͱͷείΞͷՃॏฏ‫ۉ‬ΛऔΔɽ͜Ε͸ɼଟ֯తʹྨࣅੑΛଊ͑ɼଟ༷ͳީิΛఏࣔ͢Δ͜ͱɼ‫ͼٴ‬ɼॏΈௐ੔ʹΑͬͯɼϢʔβʔͷҙ޲Λ൓ө͢Δ͜ͱΛҙਤ͍ͯ͠ΔɽॏΈֶश͸ɼ্‫ه‬ͷॏΈΛௐ੔͢Δ෦෼Ͱ͋Γɼఏࣔީิʹର͢ΔϢʔβʔϑΟʔυόοΫ͔Βɼ‫Ͳࡏݱ‬ͷಛ௃ྔΛྫ͑͹ɼʮϚʔνʯͱ͍͏୯‫ޠ‬͸ʮं໊ʯͷଞʹɼʮߦॏࢹͯ͠ީิΛఏࣔ͢΂͖͔Λߋ৽͢Δɽֶशํུͱਐ‫ۂ‬ʯ΍ʮ౎಺ࢲཱେֶʯͱ͍ͬͨଟٛੑ͕͋Γɼจͯ͠͸ɼϢʔβʔ͕ਖ਼ղͱͨ͠ఏࣔީิͷ͏ͪɼείॻ಺Ͱͷ࢖ΘΕํʹґଘܾͯ͠ΊΒΕͳ͚Ε͹ͳΒͳΞ͕࠷খͷ΋ͷΛ࠷େԽ͢Δɽ͜ΕʹΑͬͯɼաֶशʮ೔͍ʢiʣɽ·ͨɼं໊ͷʮϚʔνʯͰ͋ͬͨͱͯ͠΋ɼΛ๷͗ͳ͕Β༩͑ΒΕͨਖ਼ղީิશମͷείΞΛ্͛ɼ࢈ंʯͰ΋͋Γɼʮ೔ຊंʯͰ΋͋ΓɼʮίϯύΫτΧʔʯະ஌ͷਖ਼ղ୯‫ͯ͠ͱิީ͕ޠ‬ఏࣔ͞ΕΔͷΛૂͬͨɽͰ΋͋ΔΑ͏ʹɼෳ਺ͷ‫͔఺؍‬ΒҟͳΔ·ͱΊํ͕Մ࣮‫Ͱݧ‬͸ɼंͷࣄ‫ނ‬ɾ‫ނ‬োʹؔ͢Δ࣮ࡏσʔλͰ‫ূݕ‬

ೳͰ͋Δʢiiʣɽͭ·Γʮ͋Δ୯‫͕ޠ‬ɼͲ͏͍ͬͨ୯‫ޠ‬Λߦ͍ɼ‫ط‬ଘख๏ͱൺ΂ɼఏҊख๏͕ࣙॻߏஙͷޮ཰ͱ‫ʹڞ‬ɼͲͷΑ͏ͳࣙॻʹొ࿥͞ΕΔ͔ʯ͸෼ੳஈ֊Խʹ‫د‬༩͢Δ͜ͱΛ֬ೝͨ͠ɽ·Ͱෆ໌Ͱɼ༧ΊશͯΛ༻ҙ͢Δͷ͸ࠔ೉Ͱ͋Δɽ

2ςΩετϚΠχϯάͷ‫ݱ‬৔ʹ͓͍ͯɼ͜͏ͨ͠୯‫ޠ‬ࣙॻ͸ϢʔβʔʹΑͬͯਓखͰߏங͞Εɼ࡞‫ۀ‬ෛՙͷߴ͍߲໨ͱͳ͍ͬͯΔ [8]ɽ͔͠͠ͳ͕Βɼ෼ੳಈ‫ػ‬ʹΑͬͯࣙॻ͕ఆٛ͞ΕΔͷͰɼϢʔβʔͷհೖ͸ෆՄܽͰ͋ΔɽͦͷͨΊɼ‫ݱ‬৔͔Β͸ɼจॻͱ͍͔ͭ͘ͷϑΟʔυόοΫΛ‫ʹݩ‬ɼ‫ߏࡏݱ‬ங͍ͯ͠ΔࣙॻͷੑຊষͰ͸ɼྨࣅੑΛଟ֯తʹଊ͑ɼ͔ͭɼϢʔβʔͷҙ޲Λ൓өͰ͖ΔείΞ‫ͱࢉܭ‬ϑΟʔυόοΫ͔ΒͷॏΈֶशʹ͍ͭͯઆ໌͢ΔɽҎ߱Ͱ͸ʮҟͳΔಛ௃ྔؔ਺Λ࣋ͭಠཱϞσϧʯͷ͜ͱΛʮϞσϧʯͱ‫Ϳݺ‬ɽ

2.1࣭Λ࡯஌ͯ͠ɼؔ࿈͠͏Δ୯‫ิީޠ‬Λఏࣔ͢ΔΑ͏ͳγεςϜ͕‫ٻ‬ΊΒΕ͍ͯΔ [8]ɽఏҊख๏είΞ‫ࢉܭ‬ఏҊख๏͸ɼ|M | ‫ݸ‬ͷϞσϧ͔ΒͳΔू߹ M ɼ‫ٴ‬ͼɼ֤ϞσϧʹରԠ͢ΔॏΈ w = (w1 , w2 , · · · , w|M | )― 1273 ―Copyright(C) 2019 The Association for Natural Language Processing.
All Rights Reserved.

Λ࣋ͪɼ୯‫ ޠ‬a ͱ୯‫ ޠ‬b ͷείΞ͸ɼ֤Ϟσϧ Mi ʹAlgorithm 1 ֶशΞϧΰϦζϜΑΔίαΠϯྨࣅ౓ͷՃॏฏ‫͢ͱۉ‬Δʢࣜ 1ʣɽfi ͸

INPUT : γʔυ୯‫ ޠ‬sɼਖ਼ղ୯‫ ߹ूޠ‬P ɼ‫ޡ‬Γ୯‫߹ूޠ‬
NOUTPUT : ߋ৽‫ޙ‬ͷ w

Ϟσϧ Mi ͷಛ௃ྔؔ਺Ͱಛ௃ྔϕΫτϧΛฦ͢ɽcos͸ೋͭͷϕΫτϧͷίαΠϯྨࣅ౓Λࢉग़͢ΔɽZi ͸Ϟσϧ Mi ಺Ͱͷ z ஋ਖ਼‫ن‬ԽΛද͓ͯ͠Γɼͨͩ͠ɼ

SET α = 0.1, K = 50
1SET w, s.t. wi =|M |for i = 0 to K do
p = min{score(s, pi , w), pi ∈ P }n = score(s, random(N ), w)
dp
dn αw←w+∗α−∗dw
dw 2wiwi = !
w
end forਖ਼‫ن‬Խ‫ޙ‬ͷෛ஋͸ 0 ʹ‫ؙ‬ΊΔɽ
!Mwi ∗ Zi (cos(fi (a), fi (b)))
score(a, b, w) = i(1)|M |ҟͳΔಛ௃ྔΛ࣋ͭϞσϧΛಠཱʹѻ͏͜ͱʹΑͬͯɼ֤ಛ௃ྔ͕ҟͳΔ‫͔఺؍‬Βଊ͑Δྨࣅੑ΋‫ݸ‬ผʹѻ͏͜ͱ͕Ͱ͖Δɽྫ͑͹ɼʮϚʔνʯ΍ʮMARCHʯͱ͍ͬͨಉٛ‫ޠ‬ͷ৔߹͸ɼn-gram ͷΑ͏ͳ‫ݻ‬ఆతͳจ຺ྨࣅ౓͕ॏཁͱͳΓɼҰํͰɼʮϚʔνʯʮϑΟοτʯͱ͍ͬͨτϐοΫ͕ࣅ͍ͯΔ୯‫ޠ‬ΛूΊΔ৔߹ʹํ޲ʹֶश཰ʢα, α2 ʣΛֻ͚ͯߋ৽͢Δɽͨͩ͠ɼw͸ɼจ຺Λॊೈʹଊ͑ΒΕΔ Word2Vec ͕໾ཱͭͳͲ

ͷ૯࿨͕ 1 ʹͳΔΑ͏ʹɼຖεςοϓਖ਼‫ن‬ԽΛߦ͏ɽ͕ߟ͑ΒΕΔɽՃ͑ͯɼϞσϧ͝ͱʹॏΈ෇͚Λߦ͏ͷ͸‫ʹྔࢉܭ‬࣮‫ݧ‬3͓͚Δར఺΋͋ΔɽͦΕͧΕͷίαΠϯྨࣅ౓͸ࣄલ࣮ࡏςΩετʹରͯ͠ධՁ༻ͷϢʔβʔࣙॻΛ࡞੒‫͖Ͱࢉܭ‬ɼ͔ͭɼֶश͢ΔॏΈͷ਺΋ߴʑϞσϧ਺Ͱ͠ɼఏҊख๏ʹΑͬͯࣙॻʹ‫·ؚ‬Ε͍ͯΔ୯‫ޠ‬ΛͲͷ͋ΔͨΊɼର࿩৔໘ʹ͓͚Δ‫ྔࢉܭ‬Λ཈͑ΒΕΔɽఔ౓ީิͱͯ͠ఏࣔͰ͖ΔΑ͏ʹͳΔ͔Λ‫͢ূݕ‬Δɽ

2.2ॏΈֶश

3.1σʔλϢʔβʔ͕ҙਤ͢Δ୯‫ޠ‬Λఏࣔ͢ΔͨΊʹɼϑΟʔ࣮‫ݧ‬σʔλʹ͸ɼNHTSA2 ͕ެ։͍ͯ͠Δถࠃ಺ͰυόοΫ͔Βద੾ͳॏΈ w Λ‫ٻ‬ΊΔɽϢʔβʔ͔Βͷͷंͷࣄ‫ނ‬ɾ‫ނ‬োʹؔ͢ΔࢿྉΛ࢖༻͢Δɽ͜ͷσʔϑΟʔυόοΫ͸ɼγʔυ୯‫ ޠ‬sɼਖ਼ղ୯‫ ߹ूޠ‬P ɼ‫ޡ‬λʹ͸ɼंछͳͲͷϝλ৘ใͷଞʹɼ۩ମతͳ‫ނ‬ো಺1Γ୯‫ ߹ूޠ‬N Λࢦ͢ ɽs ‫ ͼٴ‬P ͷ୯‫ޠ‬͸ɼϢʔβʔ༰ʹؔ͢Δ‫ه‬ड़͕‫·ؚ‬Ε͓ͯΓɼͦͷ෦෼Λର৅ςΩ͕‫ٻ‬ΊΔࣙॻʹ‫·ؚ‬ΕΔ୯‫Ͱޠ‬ɼN ͷ୯‫ޠ‬͸‫·ؚ‬Εετͱ͢Δɽ2016 ೥Ҏ߱ͷ 7,922 ΤϯτϦʔΛର৅ͳ͍୯‫ͳͱޠ‬Δɽྫ͑͹ɼϢʔβʔ͕ंछ໊ͷࣙॻΛͱ͠ɼ૯จ਺͸ 51,347ɼ૯୯‫਺ޠ‬͸ 945,877 Ͱ͋Δɽߏங͢Δ৔߹ɼs ʹ͸ʮϚʔνʯɼP ʹ͸ʮϑΟοτʯ͜ͷςΩετʹରͯ͠ɼ࣮ࡍͷ෼ੳ৔໘Ͱ૝ఆ͠͏ΔʮϓϦ΢εʯɼN ʹ͸ʮΤϯδϯʯʮτϤλʯͳͲ͕ߟ̒ͭͷ໊ࢺࣙॻʢද 1ʣΛ࡞੒͠ɼධՁʹ༻͍Δɽจ͑ΒΕΔɽͦͯ͠ɼw ͷߋ৽ʹΑͬͯɼʮεΠϑτʯ΍‫ͼٴ‬୯‫ޠ‬ͷ෼ׂɼ඼ࢺλά෇͚ɼ܎Γड͚ղੳ͸શͯʮϑΥϨελʯͷείΞ͕ߴ͘ͳΓɼީิͱͯ͠ఏࣔ

UDpipe[6]3 Ͱߦ͏ɽ·ͨɼఏࣔީิ͸඼ࢺλάͷա൒͞ΕΔ͜ͱ͕‫ظ‬଴͞ΕΔɽ਺͕ʮNOUNʢ໊ࢺʣʯ͋Δ͍͸ʮPROPNʢ‫ݻ‬༗໊ఏҊ͢Δֶशํུ͸ɼ(A) γʔυ୯‫ ޠ‬s ʹର͢Δਖ਼ࢺʣʯͱਪఆ͞Εͨ୯‫Ͱޠ‬༧ΊߜΔɽղ୯‫ ߹ूޠ‬P ͷ࠷௿είΞͷ࠷େԽɼ(B) ‫ޡ‬Γ୯‫ूޠ‬߹ N ͷείΞͷશମతͳ࠷খԽɼͷ̎఺͔ΒͳΔɽॏΈֶशͷٙࣅίʔυ͕ΞϧΰϦζϜ 1 Ͱ͋Δɽ·ͣɼֶश཰ αɼߋ৽ճ਺ K ɼॏΈ w Λॳ‫ظ‬Խ͢Δɽֶश཰ɾߋ৽ճ਺͸ͦΕͧΕ 0.1ɾ50 ͱ‫ݧܦ‬తʹܾΊɼॏΈ͸‫ۉ‬౳ׂΓͱͨ͠ɽ֤ߋ৽εςοϓͰ͸ɼ౰֘εςοϓʹ͓͚Δ P ͷதͰͷ࠷௿είΞ p ͱ N ͔ΒϥϯμϜʹબΜͩ୯‫ޠ‬ͷείΞ n ΛಘΔɽͦΕͧΕͷޯ
dp dn഑ʢ dw, dw ʣΛ༻͍ͯɼp ͸্͕Δํ޲ʹɼn ͸Լ͕Δ1 N ͸ඞਢͰ͸ͳ͘ɼ༩͑ΒΕͳ͔ͬͨ৔߹͸ؔ࿈ಈ࡞ΛશͯεΩοϓ͢Δɽ― 1274 ―ࣙॻEngineExteriorInterior
DriverCarnameCompany*

ද 1: ධՁࣙॻҰཡ಺༰਺* ྫΤϯδϯؔ࿈ 27engine, gasoline֎૷ؔ࿈32
light, bumper಺૷ؔ࿈62
audio, handleӡస੮ؔ࿈
28
handle, hornंछ໊
79
prius, tundraձ໊ࣾ
58
toyota, bmw

਺͸σʔλͷશจΛ࢖ͬͨ৔߹ͷ΋ͷɽσʔλΛ෼ׂͨ͠ࡍ͸ɼ෼ׂσʔλͷதͰස౓͕ 5 Ҏ্ͷ୯‫ޠ‬ΛࣙॻΤϯτϦʔͱͯ͠࢖༻ɽ

2 https://www.nhtsa.gov/ʢ2018 ೥ 9 ݄ 27 ೔μ΢ϯϩʔυʣ
3 http://hdl.handle.net/11234/1-1659

Copyright(C) 2019 The Association for Natural Language Processing.
All Rights Reserved.

ख๏mix
mix+gradmix+gradneg
ngheadw2vsvmsvmw

ද 2: ൺֱख๏ུ֓Ճॏฏ‫ۉ‬Ճॏฏ‫ ۉ‬+ ॏΈֶशʢਖ਼ྫʣ
Ճॏฏ‫ ۉ‬+ ॏΈֶशʢਖ਼ྫɾෛྫʣ
n-gramhead-dependency
Word2Vec
SVM ͰॏΈֶश + είΞϦϯάSVM ͰॏΈֶश + Ճॏฏ‫ۉ‬ʣ(II)γʔυ୯‫ʹޠ‬ରͯ͠ީิΛ 10 ‫ͭͣݸ‬ఏࣔ͠ɼ100‫ݸ‬ͷ୯‫ʹޠ‬ΞϊςʔγϣϯΛࢪ͢ɽ֤εςοϓʹ͓͍ͯɼࣙॻ୯‫ޠ‬ͷԿׂΛΧόʔग़དྷ͍ͯΔ͔ΛධՁ͢Δɽ
ֶश͋ΓͷϞσϧ͸εςοϓ͝ͱʹֶशΛߦ͏ɽ

3.4

݁Ռ

ධՁ (I) ͷ݁Ռ͕ਤ 1 Ͱ͋ΔɽఏҊख๏͸͍ͣΕͷ৚݅Ͱ΋୯Ұಛ௃ྔɼ͓ΑͼɼSVM ΑΓ΋ଟ͘ͷਖ਼ղ୯‫ޠ‬ΛఏࣔͰ͖͍ͯΔɽͨͩ͠ɼॏΈֶशͷޮՌ͸Ξϊ

3.2Ϟσϧ

ςʔγϣϯ਺͕ 10 ͷ࣌͸‫ݟ‬ΒΕͣʢਤ 1 ࠨʣɼ20 Ҏ্ຊ࣮‫Ͱݧ‬͸ɼn-gramɾBoWɾPMIɾhead-dependecyɾͷΞϊςʔγϣϯΛ༩͑Δ͜ͱͰֶशʹΑͬͯީิத

child-dependencyɾWord2Vec ͷ̒ͭͷಛ௃ྔΛ༻͍ͷਖ਼ղ୯‫͑૿͕਺ޠ‬Δʢਤ 1 தӈʣɽ·ͨɼಛʹσʔλΔɽ2.1 અͰड़΂ͨΑ͏ʹɼ୯‫ؒޠ‬ͷείΞ͸ɼ͜Εྔ͕গͳ͍࣌ʹͦͷޮՌ͕‫ݦ‬ஶͰ͋Δʢ< 20000 จʣɽ

Β 6 ͭͷಠཱͨ͠ಛ௃ྔผϞσϧ͔ΒಘΒΕΔɼ6 ͭ

ධՁ (II) ͷ݁Ռ͕ਤ 2 Ͱɼಉਤࠨ͕σʔλ͕খ͍͞৔ͷίαΠϯྨࣅ౓ΛՃॏฏ‫ͨ͠ۉ‬΋ͷͰ͋Δɽ߹ɼಉਤӈ͕σʔλ͕େ͖͍৔߹Ͱ͋Δʢ5000 จ vs.

4ൺֱख๏ͱͯ͠ද 2 ͷ 8 ͭΛऔΓ্͛ΔɽఏҊख๏

50000 จʣɽ͍ͣΕͷ৔໘Ͱ΋ఏҊख๏ͷΧόʔ཰͕͸ɼmixɾmix+gradɾmix+gradneg ͷ 3 ͭͰ͋Γɼॏߴ͘ਪҠ͍ͯ͠Δɽσʔλ͕খ͍͞৔߹͸ɼ̏εςοΈֶशͷ༗ແʹ͓͍ͯҟͳΔɽ୯Ұͷಛ௃ྔͷΈΛ࢖ϓ໨ʢi.e. 30 ୯‫ʹޠ‬ΞϊςʔγϣϯΛࢪͨ࣌͠఺ʣ͔

͏Ϟσϧ͕ ngɾheadɾw2v ͰɼͦΕͧΕͷಛ௃ྔͷΒɼॏΈֶशϞσϧʢmix+grad, mix+gradnegʣֶ͕ΈʹΑΔίαΠϯྨࣅ౓ͰީิΛఏࣔ͢Δ ɽՃ͑ͯɼशͳ͠ͷϞσϧʢmixʣΛ্ճ͍ͬͯΔɽσʔλ͕େ͖෼ྨ໰୊ͱֶͯ͠श͢ΔϞσϧʹ svmɾsvmw Λ‫͛ڍ‬ɼ͘ͳΔͱֶशޮՌ͸ബΕΔ͕ɼεςοϓ͕ਐΉʹͭΕͪ͜ΒͰ͸ɼmix ͱಉ͡ 6 ͭͷϞσϧʹΑΔίαΠͯɼΞϊςʔγϣϯ਺͕૿͑ɼֶशʹΑͬͯΑΓଟ͘ϯྨࣅ౓Λೖྗͱ͢Δ̎஋෼ྨ‫ث‬Λֶश͢Δɽsvmɾͷਖ਼ղ୯‫ޠ‬ΛΧόʔͰ͖ΔΑ͏ʹͳ͍ͬͯΔɽσʔλsvmw ‫ʹڞ‬ɼ༩͑ΒΕͨਖ਼ྫɾෛྫ͔ΒॏΈΛֶश͕͢খ͍࣌͞͸ SVM ΋ͦΕͳΓͷΧόʔ཰ʹୡ͍ͯ͠Δ఺͸‫ڞ‬௨͍ͯ͠Δ͕ɼͦͷ࢖༻ํ๏͕ҟͳΔɽsvmΔ͕ɼσʔλ͕େ͖͘ͳΔͱεςοϓ‫ޙ‬൒Ͱͷ৳ͼ͕͸είΞϦϯά΋෼ྨ‫ث‬ͷਪఆ֬཰஋Λྲྀ༻͢Δ͕ɼѱ͘ͳΔɽ͓ͦΒࣙ͘ॻͷҰ෦ͷ୅දతͳ୯‫ʹޠ‬Φʔ

svmw Ͱ͸ֶशͨ͠ॏΈΛఏҊख๏ͷείΞϦϯάؔόʔϑΟοτͯ͠͠·͍ɼϚΠφʔͳ୯‫ޠ‬Λ͏·͘ఏ਺Ͱ༻͍ΔɽࣔͰ͖ͳ͍ͨΊͱߟ͑ΒΕΔɽ

53.3

ධՁ

4෼ੳຊ࣮‫Ͱݧ‬͸̎छͷγϛϡϨʔγϣϯͰධՁ͢Δɽ͜ຊख๏͸ɼϢʔβʔϑΟʔυόοΫʹԠͯ͡ಉ͡୯͜Ͱɼީิʹରͯ͠ɼࣙॻΛࢀরͯ͠ਖ਼ղީิ P ͱ‫ʹޠ‬ରͯ͠΋ҟͳΔީิΛఏࣔ͢Δ͜ͱ͕‫ظ‬଴͞ΕΔɽ‫ޡ‬Γީิ N ʹ‫ػ‬ցతʹৼΔ͜ͱΛʮΞϊςʔγϣϯʯ͜͜Ͱ͸શจΛ༻͍ͨ৔߹ͷ mix+gradneg Ϟσϧͷͱ‫Ϳݺ‬ɽৼΔ෣͍Λ෼ੳ͢Δɽγʔυͱͯ͠ʮTOYOTAʯΛ༩γʔυ୯‫ʹޠ‬ର͢Δॳ‫ิީظ‬ʢ10ɾ20ɾ30 ‫ݸ‬ʣʹ͑Δͱɼॳ‫ͯ͠ͱิީظ‬ද 3 ͷࠨྻ͕ಘΒΕΔɽʮ೔ຊΞϊςʔγϣϯΛࢪͨ͠‫ޙ‬ɼ࣍ͷ 30 ‫ݸ‬ͷީิதʹ͍ंʯࣙॻΛ૝ఆͯ͠ʮHondaʯ
ʮSUBARUʯʮ LEXUSʯͭ͘ਖ਼ղީิ͕‫·ؚ‬Ε͍ͯΔ͔ΛධՁ͢Δɽֶशͳ͠Λਖ਼ྫͱͯ͠༩͑ͨ৔߹ͷީิ͕ද 3 தྻͰɼʮ೔ຊ

ͷख๏ʢmix, ng, head, w2vʣ͸ͦͷ··࣍ͷީิΛंʴถࠃंʯࣙॻΛ૝ఆͯ͠ɼ͞ΒʹʮFordʯʮChevro-ఏࣔ͠ɼֶश͋Γͷख๏ʢmix+grad, mix+gradneg,letʯʮChryslerʯʮGMCʯΛਖ਼ྫͱͯ͠༩͑ͨ৔߹ͷsvm, svmwʣ͸ॳ‫΁ิީظ‬ͷΞϊςʔγϣϯ݁Ռ͔Β࣍ͷީิ͕ද 3 ӈྻͰ͋Δɽ೔ຊंͷΈΛ༩͑ͨ৔ֶशͨ͠‫ʹޙ‬ɼ࣍ͷީิΛఏࣔ͢Δɽ߹͸ɼ
ʮAcuraʯ΍ʮMitsubishiʯͱ͍ͬͨ৽ͨͳ೔ຊ

(I)ंϝʔΧʔ͕ఏࣔ͞ΕΔͷʹରͯ͠ɼถࠃं΋༩͑Δ
4 n-gram ͸̍ʙ̑·ͰɼBoWɾPMI ͸΢Οϯυ΢෯̑Ͱఆٛ

ͨ͠ɽn-gramɾ BoWɾhead-dependencyɾchild-dependency ͸Χ΢ϯτϕʔεͷεύʔεͳಛ௃ྔͰ͋Δɽ
5 BoWɾPMIɾchild-dependency ʹ͍ͭͯ΋࣮‫ݧ‬Λߦ͕ͬͨɼશମͷ݁࿦ʹӨ‫͍ͳ͕ڹ‬ͷͰলུ͢ΔɽͱʮDealerʯʮDealershipʯͱ͍ͬͨंϝʔΧʔʹؔ͢
ΔҰൠతͳ୯‫͕ޠ‬ఏࣔ͞ΕΔɽ͜ͷΑ͏ʹϢʔβʔͷ― 1275 ―Copyright(C) 2019 The Association for Natural Language Processing.
All Rights Reserved.

ਤ 1: ֶश‫ޙ‬ͷฏ‫ۉ‬ਖ਼ղ୯‫਺ޠ‬ɽY ͕࣠ฏ‫ۉ‬ਖ਼ղ୯‫ޠ‬ఏࣔ਺ɼX ͕࣠࢖༻จ਺ɼϑΝηοτ͕Ξϊςʔγϣϯ਺ɽਤ 2: εςοϓ͝ͱͷਖ਼ղ୯‫ޠ‬Χόʔ཰ఏҊख๏ʹؔ࿈͢Δ‫ڀݚ‬෼໺ͱͯ͠͸ɼ՝୊ʹද 3: ʮTOYOTAʯʹର͢Δֶशલɾֶश‫ิީޙ‬ॳ‫ิީظ‬Honda
FordKIAChevrolet
Hyundai
Volkswagen
SUBARU
LEXUS
Chrysler
GMC೔ຊंͰֶश‫ޙ‬
Audi
Cadilac
Jayco
HyundbeVW
Jeep
VolvoAcuraMitsubishi
Lincorn೔ຊɾถࠃंͰֶश‫ޙ‬
DealerAudi
VW
Cadilac
Jeep
Dealership
GMVolvo
HyundbeMercedes(ࠨ) ॳ‫ิީظ‬ɼ(த) ೔ຊϝʔΧʔͰֶश‫ޙ‬ͷީิɼ(ӈ) ೔ຊɾ ถࠃϝʔΧʔͰֶश‫ޙ‬ͷީิ .
ଠࣈ͸೔ຊंɺࣼମ͸ถࠃं.

ϑΟʔυόοΫʹ൓Ԡͯ͠ɼಈతʹΑΓదͨ͠ީิΛฦ͢͜ͱ͕Ͱ͖͍ͯΔɽ

5͓ΘΓʹຊߘͰ͸ςΩετϚΠχϯάʹ͓͚Δࣙॻߏஙͷ՝୊Λఏࣔ͠ɼͦͷղܾࡦͱͯ͠ɼର࿩‫ܕ‬ͷࣙॻߏஙࢧԉख๏ΛఏҊͨ͠ɽࣙॻͷ൒ࣗಈɾࣗಈߏங͸ɼಉٛ‫ޠ‬΍্Ґ‫ޠ‬ɾԼҐ‫͍ͨͬͱޠ‬Ұൠత୯‫ޠ‬ੑ࣭ͱͯ͠ͷࢼΈ͸ͳ͞Ε͖͍ͯͯΔ͕ [3]ɼࠓճͷΑ͏ʹυϝΠϯ΍Ϣʔβʔʹґଘͯ͠ఆٛ͞ΕΔ‫ڥ؀‬ԼͰͷख๏͸΄ͱΜͲٞ࿦͞Ε͍ͯͳ͍ɽͲͷΑ͏ʹ‫ط‬ଘͷٕज़Λద༻Ͱ͖Δͷ͔ɼ͋Δ͍͸ɼͰ͖ͳ͍ͷ͔ΛධՁσʔλͷ੔උͱ‫͕ͱ͍ͯͬ͘͜ߦʹڞ‬՝୊Ͱ͋ΔɽԠͯ͡ద੾ͳಛ௃ྔΛબ୒͢Δ͜ͱΛࢦ͢ Feature

Selection[7] ΍ Feature Fusion[4] ͕‫͛ڍ‬ΒΕΔɽຊߘͰ͸ɼࣙॻߏஙͱ͍͏໨తɾ৔໘ʹ͓͚Δಛ௃ྔઃ‫ܭ‬ɼ‫ͼٴ‬ɼͦͷબ୒ํུΛٞ࿦ͨ͠ɽࢀߟจ‫ݙ‬[1] Desheng Cai, Jingjing He, Gong-Qing Wu, and Xuegang Hu.
Synonymous entity recognition based on feature fusion. IEEEInternational Conference on Big Knowledge, 2017.
[2] Shantanu Godbole, Indrajit Bhattacharya, Ajay Gupta, andAshish Verma. Building re-usable dictionary repositories forreal-world text mining. In Proceedings of the 19th ACM Conference on Information and Knowledge Management, 2010.
[3] Dishan Gupta, Jaime Carbonell, Anatole Gershman, Steve
Klein, and David Miller. Unsupervised phrasal near-synonym
generation from text corpora. In Proceedings of the TwentyNinth AAAI Conference on Artificial Intelligence, 2015.
[4] Utthara Gosa Mangai, Suranjana Samanta, Sukhendu Das,and Pinaki Roy Chowdhury. A survey of decision fusion andfeature fusion strategies for pattern classification. IETE Technical Review, Vol. 27, No. 4, 2010.
[5] T. Nasukawa and T. Nagano. Text analysis and knowledge
mining system. IBM Syst. J., Vol. 40, No. 4, October 2001.
[6] Milan Straka and Jana Straková. Tokenizing, pos tagging,lemmatizing and parsing ud 2.0 with udpipe. In Proceedingsof the CoNLL 2017 Shared Task: Multilingual Parsing fromRaw Text to Universal Dependencies, 2017.
[7] B. Xue, M. Zhang, W. N. Browne, and X. Yao. A survey
on evolutionary computation approaches to feature selection.
IEEE Transactions on Evolutionary Computation, Vol. 20,
No. 4, 2016.
[8] ఩࠸ಹਢ઒. ςΩετϚΠχϯάΛ࢖͏ٕज़/࡞Δٕज़ : ‫ٕૅج‬ज़ͱద༻ࣄྫ͔Βಋ͘ຊ࣭ͱ‫༻׆‬๏. NetLibrary, 2009.

― 1276 ―Copyright(C) 2019 The Association for Natural Language Processing.
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