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௚ᄻੜ੒σʔληοτΛ༻͍ͨൺᄻද‫ݱ‬ͷࣗಈબ୒ຶੴ ༙ే†ౢా ࿨޹†† ‫۝‬भ޻‫ۀ‬େֶେֶӃ ৘ใ޻ֶ෎ ઌ୺৘ใ޻ֶઐ߈ɼ˟ 820-8052 ෱Ԭ‫ݝ‬൧௩ࢢ઒௡ 680-4
E-mail: †{y_mitsuishi,shimada}@pluto.ai.kyutech.ac.jp͋Β·͠ൺᄻ͸৺৅Λར༻ͯ͠આ໌΍‫ه‬ड़Λ෼͔Γ΍͘͢͠ɼ‫ڠ‬ௐ΍‫ތ‬ுͷޮՌ͕͋ΔɽฏқͳจΛൺᄻද‫ݱ‬Λ‫ؚ‬Ήจʹॻ͖‫͑׵‬Δ͜ͱͰ৘ใΛΑΓԁ‫఻ʹ׈‬ୡ͢Δ͜ͱ͕ՄೳʹͳΔɽຊ‫Ͱڀݚ‬͸͞·͟·ͳจʹରԠͨ͠ൺᄻॻ͖‫͑׵‬Λߦ͏ͨΊʹɼจ຺ʹదͨ͠ൺᄻද‫ݱ‬ΛࣗಈͰબ୒͢Δख๏ʹ͍ͭͯఏҊ͢Δɽ·ͣɼൺᄻબ୒Ϟσϧͷֶशʹ
༻͍Δ௚ᄻੜ੒σʔληοτͷ࡞੒͢Δɽൺᄻͷద੾͞ (ൺᄻ౓) Λ༻͍ͯɼจ຺ʹద͍ͯ͠Δൺᄻද‫ݱ‬Λ‫ؚ‬ΉσʔλΛऩू͢Δɽࣗಈൺᄻબ୒Λߦ͏ͨΊʹɼNeural Network ʹΑΔൺᄻબ୒ϞσϧΛఏҊ͠ɼ࡞੒ͨ͠σʔληοτͰֶश͠ɼࣗಈൺᄻબ୒Λߦ͏ɽ࣮‫Ͱݧ‬͸ൺᄻબ୒γεςϜΛ༻͍ͨൺᄻબ୒ͷద੾͞ʹ͍ͭͯ‫͠ূݕ‬ɼ‫ط‬ଘͷ࿈૝ࣙॻΛ༻͍Δख๏Ͱ͸ߦ͑ͳ͍৽‫ن‬ੑͷ͋Δൺᄻॻ͖‫͑׵‬ͷ༗ޮੑΛ֬ೝͨ͠ɽΩʔϫʔυ ௚ᄻੜ੒σʔληοτɼࣗಈબ୒ɼਂ૚ֶश

Selection of Figurative Expression Using Automatically Generated Data Set
Yuto MITSUISHI† and Kazutaka SHIMADA†† Graduate School of Computer Science and System Engineering, Kyushu Institute of Technology, 680-4,Kawazu, Iizuka, Fukuoka, 820-8502 Japan
E-mail: †{y_mitsuishi,shimada}@pluto.ai.kyutech.ac.jp
Abstract Figurative expressions are one of eﬀective approaches to lead readers to understand. In this paper,
we propose a selection method of a suitable ﬁgurative expression about an adjective in a sentence by using neuralnetworks (LSTM with attention). NNs usually need a large amount of training data for generating a model. We ﬁrst
construct the training date set automatically by computing a simile-likelihood score of each ﬁgurative candidate.
Next, we select sentence-ﬁgurative expression pairs with high simile-likelihood scores as the training data. Then, wegenerate a LSTM-based selection model by using the training data. We obtained sentences with some interesting
ﬁgurative expressions that cannot be selected by dictionary-based methods.
Key words Automatically Generated Data Set, Automatic Selection, Deep Learningೳͳ‫ޠݴ‬Ϟσϧͱͯ͠஌ΒΕ͍ͯΔ Neural Network (NN) Λ

1. · ͑ ͕ ͖‫ͨ͠༻׆‬จॻ෼ྨϞσϧ [4] ͷ‫ߦ͕ڀݚ‬ΘΕ͍ͯΔɽNN ͷҰจֶతͳද‫͍͓ͯʹݱ‬ൺᄻ͸৺৅Λར༻ͯ͠આ໌΍‫ه‬ड़Λ෼छͰ͋Δ Recurrent Neural Network (RNN) ʹΑΓɼจΛ୯‫ޠ‬͔Γ΍͘͢͠ɼ‫ڠ‬ௐ΍‫ތ‬ுͷޮՌ͕͋ΔɽฏқͳจΛൺᄻද‫ݱ‬ͷ࣌‫ྻܥ‬σʔλͱͯ͠༻͍Δ͜ͱͰจ຺৘ใΛߟֶྀͨ͠श͕Λ‫ؚ‬Ήจ΁ͱॻ͖‫͑׵‬Δ͜ͱʹΑΓɼ৽͍֓͠೦Λཧղͤ͞ΔͰ͖Δɽ͜ͷ RNN Λ༻͍ͨจॻ෼ྨϞσϧΛԠ༻͠ɼൺᄻͷ͜ͱ͕Ͱ͖ɼ৘ใΛΑΓԁ‫఻ʹ׈‬ୡ͢Δ͜ͱ͕ՄೳʹͳΔ [1]ɽੜ੒Λ͋Δจʹର͢Δద੾ͳൺᄻද‫ݱ‬Λબ୒͢Δ໰୊ͱ͓͖͔ൺᄻද‫ݱ‬Λ‫ؚ‬Ήจ΁ͷॻ͖‫͑׵‬Λߦ͏‫Ͱڀݚ‬͸ɼओʹ֬཰ੜ੒͑Ε͹ࣗಈॻ͖‫͕͑׵‬ՄೳʹͳΔɽ͢ͳΘͪɼRNN ෼ྨϞσϞσϧ΍ҙຯωοτϫʔΫΛ࡞੒͠ɼॻ͖‫͑׵‬ΒΕΔ‫͔ޠ‬Β࿈ϧΛ༻͍ͨൺᄻબ୒Λߦ͏ʹ͸ɼೖྗจͱೖྗจதͷ୯‫ʹޠ‬ஔ૝͞ΕΔ໊ࢺΛ༻͍ͯॻ͖‫͑׵‬Δൺᄻද‫ݱ‬Λબ୒͢Δख๏͕ఏ͖‫͑׵‬Մೳͳൺᄻද‫ݱ‬ͷϖΞσʔλ͕ඞཁʹͳΔɽ͔͠͠ɼֶҊ͞Ε͍ͯΔ [2] [3]ɽ͔͠͠ɼ͜ΕΒͷ‫Ͱڀݚ‬͸ೖྗจͷ‫ܗ‬༰शʹ༻͍Δ͜ͱ͕Ͱ͖Δे෼ͳྔͷσʔλ͕ଘࡏ͠ͳ͍ɽ
ͦ͜Ͱɼຊ࿦จͰ͸ NN ෼ྨϞσϧΛ༻͍ͨൺᄻબ୒Λߦࢺɼ໊ࢺͷ਺͕ઃఆ͞Ε͍ͯͨΓɼ୯७ͳߏจ͔͠ॻ͖‫͕͑׵‬Ͱ͖ͳ͍ɽ͏ͨΊɼେ͖͘෼͚ͯ̎ͭͷॲཧΛߦ͏ɽ·ͣɼൺᄻબ୒Ϟσຊ‫Ͱڀݚ‬͸ɼࣗ༝ͳߏจʹରԠ͠ɼจ຺Λߟྀͨ͠ൺᄻද‫ݱ‬ϧͷֶशʹ༻͍Δ௚ᄻੜ੒σʔληοτΛ࡞੒͢Δɽ௚ᄻੜ੒Λ‫ؚ‬Ήจͷࣗಈॻ͖‫͑׵‬Λߦ͏͜ͱΛ໨తͱ͢Δɽۙ೥ɼߴ‫ػ‬σʔληοτΛ࡞੒͢Δࡍɼൺᄻจͷద੾͞ (ൺᄻ౓) Λ༻͍- 35 -—1—This article is a technical report without peer review, and its polished and/or extended version may be published elsewhere.

Copyright ©201 by IEICEͯ࡞੒͢Δɽൺᄻ౓ͱ͸ೝ஌৺ཧֶʹ͓͚ΔൺᄻͷॲཧաఔΛ޻ֶతʹऔΓೖΕͨ‫ࢉܭ‬ख๏Ͱ͋Δɽͦͯ͠ɼRNN ʹΑΔൺWikipedia㐃᝿㢮ㄒ㎡඾ᄻબ୒ϞσϧΛఏҊ͢Δɽ௚ᄻੜ੒σʔληοτʹΑΓֶशͨ͠ൺᄻબ୒ϞσϧΛ༻͍ͯൺᄻબ୒Λߦ͏ɽબ୒͞Εͨൺᄻද䐟‫ʹݱ‬ରͯ͠ਓखධՁ͠ɼఏҊͨ͠ൺᄻબ୒Ϟσϧͷ༗ޮੑΛ‫ݕ‬䐡䐠┤႘⏕ᡂ䝕䞊䝍䝉䝑䝖㞷䛾䜘䛖䛺[ᙧᐜモ]ূ͢Δɽ

[ᙧᐜືモ] 䛝䜜䛔䛺,Ύ⣧䛺㨣䛾䜘䛖䛺

2. ؔ ࿈ ‫ڀ ݚ‬䝨䜰䝕䞊䝍ⓑ䛔,Ύ䛔,⨾䛧䛔ẚ႘ᗘⓑ䛔⅊䛜୍㠃䛻ᗈ䛜䜛㞷䛾䜘䛖䛺ⓑ䛔⅊䛜୍㠃䛻ᗈ䛜䜛㞷䛾䜘䛖䛺

0.9

䛭䛾ⓑⓑ䛔⢊䛿䛖䛹䜣⢊㞷䛾䜘䛖䛺ⓑ䛔㞷䛜㝆䜛㞷䛾䜘䛖䛺

0.1ᛣ䜛䛸ᛧᛧ䛔ẕ䛜௒᪥䛿㨣䛾䜘䛖䛺䛭䛾ⓑⓑ䛔⢊䛿䛖䛹䜣⢊㞷䛾䜘䛖䛺

0.8ᛣ䜛䛸ᛧᛧ䛔ẕ䛜௒᪥䛿㻚㻚㻚㨣䛾䜘䛖䛺

0.8⃭䛧䛔ጒᛮ䛔㎸䜏䛾⃭

㨣䛾䜘䛖䛺

0.2[ᙧᐜモ]j ᛧ䛔,⃭䛧䛔,ᅗኴ䛔[ᙧᐜືモ]⊛Ⅿ䛺,䝽䜲䝹䝗䛺ฏқͳจΛൺᄻද‫ݱ‬Λ‫ؚ‬Ήจʹॻ͖‫͑׵‬Δ‫༷͕ڀݚ‬ʑߦΘΕ͍ͯΔɽAbe Β [2] ͸౷‫ܭ‬త෼ੳΛ༻͍ͯൺᄻจʹॻ͖‫ͯ͑׵‬
͍Δɽେ‫ن‬໛ͳ‫ޠݴ‬σʔλ͔Β Probabilistic LSI (PLSI) [5] ͱਤ1௚ᄻੜ੒σʔληοτͷ࡞੒ख๏͍͏֬཰ੜ੒ϞσϧΛ༻͍ͯ୯‫֓ͱޠ‬೦ؔ܎ͷ֬཰ϞσϧΛߏங͠ɼฏқͳจʹ‫·ؚ‬ΕΔ 3 ͭ‫ܗ‬༰ࢺͱɼ1 ͭͷ໊ࢺ͔Βɼ໊Ξͱɼେ‫ن‬໛ίʔύεͷจΛ༻͍ͯɼจͱൺᄻද‫ݱ‬ͷϖΞσʔࢺΛઆ໌͢Δ௚ᄻද‫ݱ‬Λੜ੒͍ͯ͠Δɽ๺ాΒ [3] ͸ిࢠࣙॻλΛ࡞੒͢Δɽզʑ͸ൺᄻจ͕Ͳͷ͘Β͍ద੾Ͱ͋Δ͔ΛଌΔͷ஌ࣝϕʔεΛ༻͍ɼஔ͖‫͔ݩ͑׵‬ΒᄻࣙΛੜ੒͠ॻ͖‫͑׵‬Λࢦඪͷൺᄻ౓ [14] ΛఏҊ͍ͯ͠Δɽຊ‫Ͱڀݚ‬͸ɼ͜ͷ‫ط‬ଘͷൺߦ͍ͬͯΔɽൺᄻจʹॻ͖‫͑׵‬ΔΞϧΰϦζϜʹ͸ೝ஌৺ཧֶᄻ౓ͷ‫ࢉܭ‬ख๏Λվྑͨ͠৽ͨͳൺᄻ౓ΛఏҊ͢Δɽ৽ͨʹఏʹ͓͚Δ࣮‫͔ݧ‬ΒಘΒΕͨൺᄻཧղɼੜ੒ʹؔ͢Δ஌‫[ ݟ‬6] Λ޻Ҋ͢Δൺᄻ౓Λ༻͍ͯɼϖΞσʔλΑΓ࡞੒ͨ͠ൺᄻจͷൺֶతʹಋೖ͍ͯ͠ΔɽRezepka Β [7] ͸ Ortony ͷൺֱཧ࿦ [8]ᄻ౓Λ‫ٻ‬ΊΔɽͦͯ͠ɼઃఆͨ͠ᮢ஋Ҏ্ͷൺᄻ౓Ͱ͋ΔϖΞΛ਺஋Ͱද͢‫ࢉܭ‬ख๏ΛఏҊ͠ɼൺᄻจͷੜ੒Λߦ͍ͬͯΔɽσʔλΛ௚ᄻੜ੒σʔληοτʹ༻͍Δɽ

Ortony ͷൺֱཧ࿦ͱ͸ᄻࣙͱඃᄻࣙͷ‫ڞ‬༗͢Δଐੑ֓೦Λ‫ݕ‬

3. 1 અͰ͸ɼจͱൺᄻද‫ݱ‬ͷϖΞσʔλ࡞੒ํ๏ʹ͍ͭͯઆࡧ͠ɼ࠷΋‫཭ڑ‬ͷ͍ۙ΋ͷΛղͱ͢Δཧ࿦Ͱ͋Δɽ৽ฉίʔύ

໌͠ɼ3. 2 અͰ͸ɼൺᄻ౓‫ࢉܭ‬ख๏ʹ͍ͭͯઆ໌͢Δɽ·ͨɼε͔Βऔಘͨ͠‫ܗ‬༰ࢺ-໊ࢺͷ bi-gram ͔Βᄻࣙͱඃᄻࣙʹؔ

3. 3 અͰ͸ɼൺᄻ౓Λߟྀͨ͠௚ᄻੜ੒σʔληοτͷ࡞੒ʹ࿈͢Δ໨త‫ޠ‬ͷ‫཭ڑ‬Λ‫ٻ‬ΊΔɽઃఆͨ͠ᮢ஋Ҏ্ͷ‫཭ڑ‬ͷᄻ͍ࣙͭͯઆ໌͢ΔɽΛਖ਼͍͠ൺᄻจͱͯ͠‫͍ͯ͠ࢉܭ‬Δɽ

3. 1 จͱൺᄻද‫ݱ‬ͷϖΞσʔλ࡞੒·ͨɼൺᄻཧղͷͨΊʹൺᄻද‫ݱ‬Λम০‫͑׵͖ॻʹޠ‬Δ‫ڀݚ‬จͱൺᄻද‫ݱ‬ͷϖΞσʔλ࡞੒ͷऔಘํ๏ʹ͍ͭͯઆ໌͢΋ߦΘΕ͍ͯΔɽࡔ‫[ ޱ‬9] ͸࿈૝֓೦ࣙॻ [10] Λ༻͍ͯൺᄻදΔɽൺᄻจ࡞੒࣌ʹஔ͖‫͔ݩ͑׵‬Β࿈૝͞ΕΔ࿈૝‫͕ޠ‬ඞཁʹ‫ݱ‬ͷॻ͖‫͑׵‬Λߦ͍ͬͯΔɽ࿈૝֓೦ࣙॻͱ͸ਓؒΛඃ‫ऀݧ‬ͳΔɽຊ‫Ͱڀݚ‬͸ɼ࿈૝ྨ‫ࣙޠ‬యʢ஫ 3ʣΛ༻͍ͯ࿈૝‫ޠ‬Λऔಘ͢ͱͯ͠େ‫ن‬໛ͳ࿈૝࣮‫ݧ‬Λߦ͍ɼܹࢗ֓೦ͱ࿈૝֓೦ͷରɼ͓Δɽ࿈૝ྨ‫ࣙޠ‬య͸‫ݴ‬༿Λࢦఆ͢Δͱɼͦͷ‫ݴ‬༿ͷྨ‫ޠ‬ɼಉٛΑͼ྆ऀؒͷ‫͕཭ڑ‬ఆٛ͞Ε͍ͯΔࣙॻͰ͋Δɽ͜ͷࣙॻΛ‫ޠ‬ɼ࿈૝‫͕ޠ‬දࣔ͞ΕΔ Web αʔϏεͰ͋Δɽ·ͣɼ࿈૝ྨ

Integrated-and-Fire Ϟσϧ [11] ͷχϡʔϥϧωοτϫʔΫ্ʹ‫ࣙޠ‬య಺ͷʮ˓˓ͷΑ͏ͳʯͱ͍͏ൺᄻද‫ݱ‬Λશͯ֫ಘ͠ɼൺ࣮૷͠ɼᄻࣙͱඃᄻࣙͷଐੑ֓೦Λ‫͢ࢉܭ‬Δɽ‫ڞ‬௨͢Δଐੑ֓ᄻද‫͔ݱ‬Β࿈૝͢Δ‫ܗ‬༰ࢺͱ‫ܗ‬༰ಈࢺΛऔಘ͢Δ (ਤ 1 - )
1 ɽ೦ͷ‫཭ڑ‬Λ‫͠ࢉܭ‬ɼ࠷΋‫཭ڑ‬ͷ͍ۙଐੑ֓೦Λஔ͖‫͑׵‬Δम০‫ޠ‬࣍ʹɼେ‫ن‬໛ίʔύε͔Β࿈૝͞ΕΔम০‫ޠ‬Λ‫ؚ‬ΉจΛ‫͢ࡧݕ‬ͱͯ͠ɼจͷॻ͖‫͑׵‬Λߦ͍ͬͯΔɽ෌ࢁΒ [12] ͸ Wikipedia

Δ (ਤ 1 - )
2 ɽͦͯ͠ɼେ‫ن‬໛ίʔύε͔Β‫ͨ͠ࡧݕ‬จͱൺᄻ͔Βऔಘͨ͠େ‫ن‬໛ίʔύεΛ༻͍ͯ୯‫ޠ‬ͷ෼ࢄද‫ݱ‬Λֶशද‫͔ݱ‬ΒϖΞσʔλΛ࡞੒͢Δ (ਤ 1 - )
3 ɽຊ‫Ͱڀݚ‬͸ɼ2018ʢ஫ 1ʣ͠ɼᄻࣙͷॻ͖‫͑׵‬Λߦ͍ͬͯΔɽ෌ࢁΒ͸ word2vecͰ‫ܭ‬ࢉͨ͠෼ࢄද‫ݱ‬Λ༻͍ͯɼᄻࣙͱඃᄻࣙʹྨࣅͨ͠‫ܗ‬༰ࢺΛಘ

೥ 4 ݄ 20 ೔෇Ͱ΢ΟΩϝσΟΞࡒஂΑΓఏ‫͞ڙ‬Ε͍ͯΔσʔλϕʔεɾσʔλʢ஫ 4ʣ͔Βೖखͨ͠΋ͷΛ༻͍Δɽ

ΔɽOrtny ͷൺֱཧ࿦Λ‫ʹݩ‬ɼ࠷΋ྨࣅ౓͕ߴ͍‫ܗ‬༰ࢺͰᄻࣙ

3. 2 ൺᄻ౓‫ࢉܭ‬ख๏Λॻ͖‫͍ͯ͑׵‬ΔɽMao Β [13] ͸୯‫ޠ‬෼ࢄද‫ ͱݱ‬Wordnetʢ஫ 2ʣ

3. 1 અͰ࡞੒ͨ͠ϖΞσʔλ͸୯७ͳϚονϯάͰੜ੒ͨ͠ͷ஌ࣝΛ࢖༻͠ɼॻ͖‫͑׵‬Λߦ͍ͬͯΔɽWordnet ͔Βᄻࣙͷ΋ͷͰ͋Γɼશͯਖ਼͍͠ൺᄻจͰ͋Δͱ͸͍͑ͳ͍ɽྫ͑͹ɼ্Ґ֓೦ͱྨٛ‫ޠ‬Λऔಘͨ͠΋ͷΛॻ͖‫͠ͱޠิީ͑׵‬ɼᄻࣙ

ਤ 1 ʹ͋ΔΑ͏ʹʮന͍ઇʯΛʮઇͷΑ͏ͳઇʯͱஔ͖‫͑׵‬ΔҎ֎ͷจ຺‫ޠ‬ͷ୯‫ޠ‬ϕΫτϧͱॻ͖‫ޠิީ͑׵‬ͷ୯‫ޠ‬ϕΫτϧͷ͸ෆద੾Ͱ͋Γɼʮࢥ͍ࠐΈͷܹ͍͠ຓʯΛʮࢥ͍ࠐΈͷَͷྨࣅ౓͕࠷΋ߴ͍΋ͷΛॻ͖‫͍༻ʹ͑׵‬ΔɽͷΑ͏ͳຓʯͱ͢Δͷ΋ద੾ͱ͸͍͑ͳ͍ɽ͢ͳΘͪɼ͋Δ‫ܗ‬

3. ௚ᄻੜ੒σʔληοτͷࣗಈ࡞੒༰ࢺ΍‫ܗ‬༰ಈࢺΛ࿈૝ྨ‫ࣙޠ‬యதͷൺᄻද‫ʹݱ‬ஔ͖‫͑׵‬ΔࡍʹຊઅͰ͸ൺᄻબ୒Ϟσϧͷֶशʹ༻͍Δ௚ᄻੜ੒σʔληοਖ਼͍͠ൺᄻͰ͋ΔϖΞσʔλͷΈΛ‫ޙ‬ड़͢Δ RNN ͷֶशσʔτͷ࡞੒ख๏Λઆ໌͢Δɽਤ 1 ʹ௚ᄻੜ੒σʔληοτͷ࡞੒λͱͯ͠༻͍ΔͨΊʹɼϖΞσʔλ಺ͷൺᄻද‫Ͳ͕ݱ‬ͷ͘Β͍ख๏Λࣔ͢ɽຊ‫Ͱڀݚ‬͸ɼ௚ᄻද‫͔ݱ‬Β࿈૝͞ΕΔम০‫ޠ‬ͷϖద੾͔Λ‫͢ࢉܭ‬Δඞཁ͕͋Δɽຊ‫Ͱڀݚ‬͸ɼೝ஌৺ཧֶʹ͓͚ʢ஫ 1ʣɿhttps://radimrehurek.com/gensim/models/word2vec.html

ʢ஫ 3ʣɿhttp://renso-ruigo.com/

ʢ஫ 2ʣɿhttps://wordnet.princeton.edu/

ʢ஫ 4ʣɿhttps://dumps.wikimedia.org/jawiki/͸ɼͦͷจͷจ຺Λߟྀ͠ͳ͚Ε͹ͳΒͳ͍ɽจ຺Λߟྀͨ͠- 36 -—2—ΔൺᄻͷॲཧաఔΛ޻ֶతʹऔΓೖΕɼൺᄻද‫ݱ‬ͷద੾͞Λଌ

tempAss
tempAssmax

simAss =Δൺᄻ౓Λಋೖ͢Δɽೇ‫ݟ‬Β [6] ͸৺ཧ࣮‫ʹݧ‬ΑΓɼൺᄻͷॲཧաఔʹ͍ͭͯҎԼͷΑ͏ʹ·ͱΊ͍ͯΔɽ

(4)

3. 2. 3 ‫౓ ى ڞ‬

ʢ I ʣ ௚ᄻɼӅᄻཧղͷग़ൃ఺͸ɼᄻࣙͱඃᄻࣙͷࣈٛͲ͓‫ ౓ىڞ‬metP M I ͸ɼ೔ৗతʹසൟʹ࢖ΘΕΔᄻࣙͱඃᄻࣙΓͷΧςΰϦతҙຯʹ͓͚Δಛ௃ରԠʹΑͬͯɼඇྨࣅੑΛ‫ݕ‬ͷ૊Έ߹Θͤ΄ͲείΞ͕ߴ͘ͳΔΑ͏ʹಋೖ͢Δɽ͜Ε͸ɼग़͢Δ͜ͱʹ͋ΔɽΧςΰϦతඇྨࣅੑ͸ൺᄻͱͯ͠ͷ࢐৽͞೔ৗతʹසൟʹ࢖༻͞ΕΔ‫ݴ‬༿Λൺᄻީิͱͯ͠༻͍ͨํ͕ʹӨ‫ڹ‬Λ༩͑Δɽཧղ͠΍͍͢ൺᄻจΛ࡞੒Ͱ͖ΔͨΊͰ͋Δɽຊ‫Ͱڀݚ‬͸೔ʢ II ʣ ᄻࣙͱඃᄻࣙͷ৘ॹɾ‫֮ײ‬తҙຯʹ͓͚Δಛ௃ରԠʹຊ‫ ޠ‬Google Ngram [15] Λ࢖༻ࣗ͠‫ݾ‬૬‫ޓ‬৘ใྔΛ‫͢ࢉܭ‬ΔɽΑͬͯɼྨࣅੑΛ‫ݕ‬ग़͢Δɽ͜ͷ৘ॹɾ‫֮ײ‬తྨࣅੑ͕ߴ͍΄C(word) ͸೔ຊ‫ ޠ‬Google Ngram ಺Ͱͷ word ͷग़‫͋Ͱ਺ݱ‬Ͳൺᄻ͕ཧղ͠΍͘͢ͳΔɽΔɽN ͸೔ຊ‫ ޠ‬Google Ngram ಺Ͱͷग़‫ͨ͠ݱ‬௚ᄻද‫਺ݱ‬Λʢ III ʣ ᄻࣙͱඃᄻࣙͷ‫ڞ‬༗ಛ௃ͷम০‫Ͱޠ‬म০͢Δͱɼ৘ॹɾද͢ɽ

‫֮ײ‬తྨࣅੑ͕ߴ·Γɼͦͷ݁Ռͱͯ͠ɼൺᄻͷཧղ༰қੑ͕

tempP M I = log2

ߴ·ΔɽC(target, source) · N
C(source)C(target)(5)

tempP M I 
tempP M I max(6)ʢ஫ 5ʣ͜ͷΑ͏ʹᄻࣙɼඃᄻࣙɼஔ͖‫ݩ͑׵‬ͷ̏ͭͷ୯‫ޠ‬ͷؔ܎ʹΑΓɼൺᄻͷ࢐৽͞΍ཧղ͠΍͕͢͞มԽ͢Δɽ

metP M I =ͦ͜Ͱɼຊ‫Ͱڀݚ‬͸ʢIʣΑΓΧςΰϦతྨࣅੑ simCatɼʢIIʣΑΓ৘ॹɾ‫֮ײ‬తྨࣅੑ simAssɼ(III) ΑΓᄻࣙͱඃᄻࣙͷ‫ڞ‬

3. 2. 4 ஔ͖‫ͱݩ͑׵‬ᄻࣙͷྨࣅ౓‫ ౓ى‬metP M I Λ‫ٻ‬Ίɼൺᄻ౓ M Λࢉग़͢Δɽ͜Εʹ৽ͨͳࢦඪͱͯ͠ஔ͖‫ͱݩ͑׵‬ᄻࣙͷྨࣅ౓ Simɼᄻࣙͷஔ͖‫͑׵‬΍͢͞ Relɼஔ͖‫ͱݩ͑׵‬ᄻࣙͷ‫ۃ‬ੑ૬ҟ൑ఆ pDif Λಋೖ͠ɼ͜ΕΒ 3 ͭͷࢦඪΛ݁߹ͨ͠ൺᄻ౓ P roposedM ΛఏҊ͢ΔɽҎ߱ɼ͜ΕΒΛॱ࣍આ໌͢Δɽஔ͖‫( ݩ͑׵‬original) ʹྨࣅ͢Δᄻࣙ΄Ͳɼஔ͖‫͑׵‬Λߦͬͨ৔߹ʹҙຯͷ૬ҧ͕ੜ·Εʹ͍͘ͱԾఆ͠ɼஔ͖‫ͱݩ͑׵‬ᄻࣙͷྨࣅ౓͕ߴ͍΋ͷ΄ͲείΞΛߴ͘͢ΔɽSim ͸ word2vec ʹΑͬͯಘͨ୯‫ޠ‬ͷ෼ࢄද‫ݱ‬ͷ cos ྨࣅ౓Λ‫ٻ‬ΊΔɽw2v(word)
͸ word2vec ʹΑΓ‫ٻ‬ΊΔ୯‫ ޠ‬word ͷ୯‫ޠ‬ϕΫτϧͰ͋Δɽ

3. 2. 1 ΧςΰϦతྨࣅੑΧςΰϦతྨࣅੑ simCat ͸ᄻࣙ (source) ͱඃᄻࣙ (target)

Sim = cos(w2v(original), w2v(target)))ͷେ·͔ͳΧςΰϦಛ௃͕ಉͩ͡ͱείΞΛߴ͘͢ΔࢦඪͰ͋

3. 2. 5 ᄻࣙͷஔ͖‫͑׵‬΍͢͞Δɽେ·͔ͳΧςΰϦಛ௃ͷྨࣅੑΛଌΔͨΊɼ೔ຊ‫ ޠ‬Word-Netʢ஫ 6ʣʹΑΓಘͨᄻࣙͱඃᄻࣙͷ্Ґ֓೦ͷҰக཰Λ‫͢ࢉܭ‬Δɽ
comCat ͸ᄻࣙɼඃᄻࣙͷ্Ґ֓೦ͷҰக਺Λද͢ɽsourceCat͸ᄻࣙͷ্Ґ֓೦਺ɼtargetCat ͸ඃᄻࣙͷ্Ґ֓೦਺Λද͢ɽશσʔλதͷ࠷େ஋ tempCatmax Ͱਖ਼‫ن‬Խͨࣜ͠ (2) ͷ஋Λεᄻ͔ࣙΒ࿈૝͞ΕΔஔ͖‫͕ݩ͑׵‬ଟ͍΄Ͳɼ༷ʑͳղऍΛ࣋ͭᄻࣙͰ͋Δɽᄻࣙͷ࣋ͭղऍ͕ଟ͍΄ͲɼͦΕͧΕͷᄻࣙͱஔ͖‫ݩ͑׵‬ͷؔ࿈ੑ͕௿͘ͳΔԾఆ͢ΔɽԼ‫ه‬ͷ 2 ͭͷจ͸ʮ͍ܰεʔπέʔεʯͷʮ͍ܰʯΛஔ͖‫ͨ͑׵‬จͰ͋Δɽ

ίΞͱͯ͠༻͍Δɽ

comCatcomCat+)tempCat = comCat(sourceCattargetCat

simCat =tempCat
tempCatmax•ӋࠜͷΑ͏ͳεʔπέʔε•ϛδϯίͷΑ͏ͳεʔπέʔεʮӋࠜͷΑ͏ͳʯͱʮϛδϯίͷΑ͏ͳʯ͸‫ʹ͍ޓ‬ʮ͍ܰʯͱ͍

(1)͏ղऍΛ࣋ͭᄻࣙͰ͋Δɽ࿈૝ྨ‫ࣙޠ‬య͔ΒʮӋࠜͷΑ͏ͳʯʹ࿈૝͞ΕΔ‫ܗ‬༰ࢺΛ‫ͨ͠ࡧݕ‬৔߹ʮ͍ܰʯͷΈ͕‫͞ࡧݕ‬ΕΔ(2)͕ɼʮϛδϯίͷΑ͏ͳʯ͸ʮ͍ܰʯͷ΄͔ʹʮখ͍͞ʯ΍ʮऑ͍ʯͳͲ΋‫͞ࡧݕ‬ΕΔɽ͜ͷ͜ͱ͔ΒɼʮϛδϯίͷΑ͏ͳʯͷ

3. 2. 2 ৘ॹɾ‫֮ײ‬తྨࣅੑ৘ॹɾ‫֮ײ‬తྨࣅੑ simAss ͸ᄻࣙͱඃᄻࣙͷಛ௃ରԠ͕ྨࣅ͍ͯ͠Δ΄ͲείΞΛߴ͘͢ΔࢦඪͰ͋Δɽ৘ॹɾ‫֮ײ‬తྨࣅੑΛଌΔͨΊɼ࿈૝ྨ‫ࣙޠ‬యΑΓಘͨᄻࣙͱඃᄻࣙͷ࿈૝‫ޠ‬ͷҰக཰Λ‫͢ࢉܭ‬ΔɽcomAss ͸ᄻࣙɼඃᄻࣙͷ࿈૝‫ޠ‬ͷҰக਺Λද͢ɽsourceAss ͸ᄻࣙͷ࿈૝‫਺ޠ‬ɼtargetAss ͸ඃᄻࣙ΄͏͕ΑΓଟ͘ͷղऍΛ࣋ͪɼʮϛδϯίͷΑ͏ͳεʔπέʔεʯ͔Βʮ͍ܰεʔπέʔεʯΛ࿈૝͠ʹ͍͘ͱ͍͑Δɽͦ͜Ͱɼᄻ͔ࣙΒ࿈૝͞ΕΔஔ͖‫ݩ͑׵‬ͷ਺ΑΓɼᄻࣙͱஔ͖‫͑׵‬‫ݩ‬ͷؔ࿈౓Λ‫ٻ‬ΊΔɽᄻࣙͱஔ͖‫ݩ͑׵‬ͷؔ࿈౓Λࣜ (8) ʹࣔ͢ɽmod ͸௚ᄻද‫͔ݱ‬Β࿈૝͞ΕΔम০‫ޠ‬ͷ਺Λද͢ɽͷ࿈૝‫਺ޠ‬Λද͢ɽશσʔλதͷ࠷େ஋ tempAssmax Ͱਖ਼‫ن‬

Rel = maxԽͨࣜ͠ (4) ͷ஋ΛείΞͱͯ͠༻͍Δɽ

comAsscomAss+)tempAss = simAss(sourceAsstargetAss(7)1, 0.01
mod(8)

3. 2. 6 ‫ۃ‬ੑ૬ҟ൑ఆஔ͖‫ͱݩ͑׵‬ᄻࣙͱͷ‫ۃ‬ੑ͕ҟͳΔ৔߹ɼจ຺ʹద͍ͯ͠ͳ

(3)͍ஔ͖‫͑׵‬Λߦ͏Մೳੑ͕͋ΔͨΊϖφϧςΟΛઃ͚Δɽຊ‫ݚ‬‫Ͱڀ‬͸ߴଜΒ [16] ͷ‫ײ‬৘‫ۃ‬ੑରԠදΛ༻͍ͯ୯‫ޠ‬ͷ‫ۃ‬ੑΛऔಘ

ʢ஫ 5ʣɿʮ‫ڪ‬Ζ͍͠฼ʯΛʮَͷΑ͏ͳ฼ʯͱॻ͖‫͑׵‬Մೳͳ৔߹
ɹɹɹᄻࣙ: َͷΑ͏ͳɼඃᄻࣙ: ฼ɼஔ͖‫ݩ͑׵‬: ‫ڪ‬Ζ͍͠ʢ஫ 6ʣɿhttp://compling.hss.ntu.edu.sg/wnja/

͢Δɽஔ͖‫ݩ͑׵‬ͷ‫ۃ‬ੑ repP ol Λࣜ (9) ʹද͠ɼᄻࣙͷ‫ۃ‬ੑ

metP ol Λࣜ (10) ʹද͢ɽsenti(word) ͸‫ײ‬৘ରԠද͔Βऔಘ- 37 -—3—ͨ͠୯‫ ޠ‬word ͷ‫ۃ‬ੑ஋Λද͢ɽmetP ol ͸ᄻࣙͷ‫ۃ‬ੑΛද͠ɼ

ද1ᄻ͔ࣙΒ࿈૝͞ΕΔ‫ ޠ‬Ass ͷ‫ۃ‬ੑͷฏ‫ۉ‬Λ༻͍Δɽஔ͖‫ݩ͑׵‬ఏҊख๏ͷ‫ۃ‬ੑ orgP ol ͱᄻࣙͷ‫ۃ‬ੑ metP ol ͱͷ‫ۃ‬ੑ͕ҟͳΔ΋ͷ͸M (ϕʔεϥΠϯ)2.76

M × Sim

3.04

M × Rel

2.91

M × pDif

2.76

P roposedM

3.16ϖφϧςΟͱͯ͠είΞΛԼ͛Δɽ

orgP ol = senti(original)1
senti(Assi )n(9)nmetP ol = log

ද2(10)(11)ฏ‫ۉ‬ධՁ஋

ൺᄻ౓෼෍ผධՁ݁Ռ෼෍

i=0⎧⎨1 (Same polarity)
pDif =⎩0 (Diﬀerent polarity)ൺᄻ౓ධՁ݁Ռฏ‫ۉ‬ධՁ஋্Ґ 30 ݅3.16தҐ 30 ݅

1.21ԼҐ 30 ݅

1.10

3. 2. 7 ൺ ᄻ ౓

P roposedM Ͱ͋ͬͨɽධՁ݁Ռ͔ΒɼϕʔεϥΠϯͷख๏Αೇ‫ݟ‬Βͷ৺ཧ࣮‫͖ͮجʹݧ‬ఏҊ͢ΔࢦඪͰ͋ΔɼΧςΰϦతΓࢦඪ SimɼRel Λ༻͍ͨख๏ͷํ͕ධՁ͕ߴ͍ͨΊɼ͜ΕΒྨࣅੑ simCatɼ৘ॹɾ‫֮ײ‬తྨࣅੑ simAssɼᄻࣙͱඃᄻࣙͷࢦඪ͕༗ޮͰ͋Δͱߟ͑ΒΕΔɽ
·ͨɼൺᄻ౓ͷେখؔ܎͕ਖ਼֬Ͱ͋Δ͜ͱ΋ॏཁͰ͋Δɽൺͷ‫ ౓ىڞ‬metP M I ͔Βɼൺᄻ౓ M Λ‫ٻ‬ΊΔɽൺᄻ౓ M Λࣜᄻ౓ͷେখؔ܎͕ਖ਼֬Ͱ͋Ε͹ɼᮢ஋ʹΑΔ଍੾ΓΛ͢Δ͜ͱ(12) ʹࣔ͢ɽͰɼϊΠζσʔλͷΈΛσʔληοτ͔Β࡟আ͢Δ͜ͱ͕ՄM = simCat + simAss + metP M I(12)ೳͰ͋Δɽͦ͜Ͱɼ࠷΋ධՁ͕ߴ͍ P roposedM ͷൺᄻ౓্Ґ·ͨɼ৽ͨʹఏҊ͢ΔࢦඪͰ͋Δஔ͖‫ͱݩ͑׵‬ᄻࣙͷྨࣅ౓Simɼᄻࣙͷஔ͖‫͑׵‬΍͢͞ Relɼஔ͖‫ͱݩ͑׵‬ᄻࣙͷ‫ۃ‬ੑ૬ҟ൑ఆ pDif Λ௥Ճͨ͠ൺᄻ౓ P roposedM Λࣜ (13) ʹࣔ͢ɽ30 จɼൺᄻ౓தҐ 30 จɼൺᄻ౓ԼҐ 30 จͦΕͧΕΛධՁऀ 3ਓ͕ಉ༷ʹ 5 ஈ֊ͰධՁͨ͠ɽධՁ݁ՌΛද 2 ʹࣔ͢ɽ্ҐɼதҐɼԼҐΛॱ൪ʹൺֱ͢Δͱɼൺᄻ౓͕௿͘ͳΔ΄Ͳฏ‫ۉ‬ධՁ஋΋௿͘ͳΔ͜ͱ͔ΒϥϯΩϯά͸ਖ਼֬Ͱ͋ͬͨͱߟ͑Β͑

P roposedM = M × Rel × pDifΔɽಛʹԼҐ 30 ݅ͷσʔλ͸ pDif ͷϖφϧςΟʹΑͬͯε(13)ͦͯ͠ɼείΞ͕ߴ͍ॱʹจ຺Λߟྀͨ͠ᄻ͕ࣙ‫·ؚ‬ΕΔจͱ

ίΞ͕Լ͕ͬͨσʔλͰ͋ΔͨΊʢ஫ 9ʣɼpDif ΋༗ޮͰ͋Δɽ

3. 3. 2 ௚ᄻੜ੒σʔληοτʹ༻͍ΔϖΞσʔλબ୒ख๏͢Δɽ‫ػ‬ցֶश͸ֶश͢ΔσʔληοτʹภΓ͕͋Δ৔߹ɼਖ਼͍͠

3. 3 ൺᄻ౓Λߟྀͨ͠௚ᄻੜ੒σʔληοτ࡞੒
3. 1 અͰ࡞੒ͨ͠ϖΞσʔλ͸ɼจ຺৘ใΛߟྀͤͣ࡞੒͍ͯ͠ΔͨΊɼશͯਖ਼͍͠ൺᄻද‫͋Ͱݱ‬Δͱ͸͍͑ͳ͍ɽશͯͷϖΞσʔλΛ NN ൺᄻબ୒Ϟσϧʹ༻͍ΔͱɼϊΠζͱͳΔσʔλΛଟֶ͘शͯ͠͠·͏ͨΊɼਖ਼֬ͳൺᄻબ୒͕ߦ͑ͳ͍ɽͦͷͨΊɼఏҊͨ͠ൺᄻ౓ʹΑΓɼจ຺Λߟྀͨ͠ϖΞσʔλΛσʔληοτʹ࢖༻͠ɼϊΠζσʔλΛ࡟আ͢Δ͜ͱ͕ॏཁʹͳΔɽ·ͣɼ৽ͨʹ௥Ճͨ͠ࢦඪͷ༗ޮੑΛ‫͢ূݕ‬Δɽͦͯ͠ɼ௚ᄻੜ੒σʔληοτʹ༻͍ΔϖΞσʔλબ୒ख๏Λઆ໌ֶशΛߦ͏͜ͱ͕Ͱ͖ͳ͍ɽ௚ᄻੜ੒σʔληοτ಺ͷൺᄻද‫ʹݱ‬ภΓ͕͋ͬͨ৔߹ɼೖྗจͷจ຺΍ஔ͖‫͑׵‬Δ‫ͳ܎ؔʹޠ‬͘ಛఆͷൺᄻද‫ݱ‬Λબ୒ͯ͠͠·͏ɽͦ͜Ͱɼબ୒͢Δൺᄻද‫ʹݱ‬ภΓ͕ͳ͍௚ᄻੜ੒σʔληοτΛ࡞੒͢ΔͨΊɼൺᄻද‫ʹͱ͝ݱ‬௚ᄻੜ੒σʔληοτ಺ʹ্‫ݶ‬஋Λઃఆ͠ɼൺᄻ౓͕ߴ͍΋ͷ͔ΒॱʹϖΞσʔλΛબ୒͢Δɽຊ࿦จͰ͸ɼൺᄻද‫্ʹͱ͝ݱ‬Ґ 500 ݅ɼൺᄻ౓͕ 0 Ҏ্ͷϖΞσʔλΛֶशσʔλͱͯ͠࢖༻͢Δɽ

4. Neural Network Λ༻͍ͨൺᄻબ୒Ϟσϧ͢Δɽ

3. 3. 1 ൺᄻ౓ͷ༗ޮੑ‫ূݕ‬ೇ‫ݟ‬Βͷ৺ཧ࣮‫͖ͮجʹݧ‬ఏҊ͢Δൺᄻ౓ M ΛϕʔεϥΠϯͱ͠ɼ৽ͨʹఏҊ͢Δࢦඪͷ༗ޮੑΛ‫ূݕ‬Λߦͬͨɽ࿈૝ྨ‫ࣙޠ‬య͔Β֫ಘͨ͠ 508 ݅ͷᄻࣙΛ༻͍ͯɼจͱൺᄻද‫ݱ‬ͷϖΞσʔλ 5,333,001 ݅Λ֫ಘͨ͠ɽ֫ಘͨ͠ϖΞσʔλΛ༻͍ͯൺᄻॻ͖‫͑׵‬Λߦ͍ɼൺᄻจΛੜ੒ͨ͠ɽੜ੒ͨ͠ൺᄻจͷൺᄻ౓Λ 5 ͭͷ‫ࢉܭ‬ख๏ʢ஫ 7ʣͰ‫ٻ‬ΊɼͦΕͧΕͷൺᄻ౓্Ґ 30จΛʹର͠ਓखͰධՁͨ͠ɽධՁऀ 3 ਓʹஔ͖‫ͨ͑׵‬ൺᄻจͷద੾͞Λ 5 ஈ֊ʢ஫ 8ʣͰධՁͨ͠ɽධՁऀͷฏ‫ۉ‬ධՁ஋Λද 1 ʹࣔ͢ɽ࠷΋ධՁ͕ߴ͍ख๏͸ຊ અ Ͱ ͸ ࣗ ಈ ൺ ᄻ બ ୒ Λ ߦ ͏ ͨ Ί ͷ Attention ෇ ͖ Bi-directional Encode-Decoder Ϟσϧ [17] ʹ͍ͭͯड़΂ΔɽAttention ͱ͸༧ଌϞσϧʹೖྗσʔλͷͲͷ෦෼ʹ஫໨͢Δ͔஌ΒͤΔ‫͋Ͱߏػ‬ΔɽBi-directional Encode-Decoder Ϟσϧ͸௨ৗͷΤϯίʔμ (RNN) ͷதؒ૚ʹೖྗจΛ‫ʹ͖޲ٯ‬ೖྗ͢ΔΤϯίʔμ (RNN) ͷதؒ૚Λ࿈݁͠ɼকདྷͷ୯‫ؔ͢ʹޠ‬Δ৘ใΛ௥Ճͨ͠΋ͷ͔Β༧ଌΛߦ͏ϞσϧͰ͋Δɽຊ‫Ͱڀݚ‬͸ RNN ͷ֦ுϞσϧͰ͋Δ LSTM ΛΤϯίʔμʹ༻͍Δɽ௚ᄻੜ੒σʔληοτͰ͜ͷϞσϧΛֶश͢Δ͜ͱͰɼೖྗจ͔
ΒᄻࣙΛબ୒͢ΔࡍͷॏཁʹͳΔ୯‫ޠ‬Λֶश͢Δɽະ஌ͷจ͔

ʢ஫ 7ʣɿM ɼM × SimɼM × RelɼM × pDif ɼP roposedMʢ஫ 8ʣɿ5. ࠷ߴධՁ (ཧղՄೳ) ʙ 1. ࠷௿ධՁ (ཧղෆՄೳ)ʢ஫ 9ʣɿpDif ͕ 0 ʹͳͬͨ৔߹ɼP roposedM (ࣜ 13) ͸ 0 ʹͳΔɽ- 38 -—4—ද3m1m2

ߙଵm3

ߙଶmnൺᄻจཧղ༰қੑධՁ݁Ռൺᄻબ୒ख๏ฏ‫ۉ‬ධՁ஋ൺᄻ౓ (P roposedM ) ͷΈ

1.66

NN ʹΑΔൺᄻબ୒Ϟσϧ

1.75

ߙସ

ߙଷ

࣋ͨ͠ൺᄻΛબ୒͢Δ͜ͱ΋ॏཁͰ͋Δɽॻ͖‫͑׵‬લͷจͷ৘ใ͕ܽམ͢Δ͜ͱͰຊདྷͷҙຯͰ఻ୡͰ͖ͳ͘ͳΔɽͦͷͨΊɼfh1

fh2fhtfh3

ஔ͖‫ݩ͑׵‬ͷ৘ใΛอ͍࣋ͯ͠ΔᄻࣙΛબ୒͢Δ͜ͱ͕ඞཁͰ
͋Δɽͦ͜Ͱɼࣗಈબ୒͞Εͨᄻ͕ࣙஔ͖‫ݩ͑׵‬ͷ಺༰Λอ࣋

bh1

w1

bh2

bh3

bht

w2

w3wt

͍ͯ͠Δ͔ΛධՁ͢Δஔ͖‫಺͑׵‬༰อ࣋ධՁ࣮‫ݧ‬Λߦͬͨɽຊ࣮‫Ͱݧ‬͸ɼ4 અͰఏҊͨ͠ൺᄻબ୒Ϟσϧͷ༗ޮੑΛ‫ূݕ‬
͢ΔͨΊɼ3 અͰఏҊͨ͠ൺᄻ౓ P roposedM ͷΈΛ༻͍ͯબ
୒͢Δख๏ͱൺֱΛߦ͏ɽҎ߱ɼ2 छྨͷਓखධՁ࣮‫͍ͭʹݧ‬

ਤ 2 Attention ෇͖ Bi-directional Encode-Decoder Ϟσϧͯઆ໌͢Δɽ

5. 1 ൺᄻจཧղՄೳੑධՁΒᄻࣙͷग़‫཰֬ݱ‬Λ‫ٻ‬Ίͯɼબ୒Λߦ͏͜ͱ͕ՄೳʹͳΔɽൺࣗಈબ୒͞ΕͨᄻࣙΛ༻͍ͯॻ͖‫ͨ͑׵‬ൺᄻจ͕ɼཧղՄೳᄻબ୒ϞσϧਤΛਤ 2 ʹࣔ͢ɽ·ͣೖྗจΛද͢‫ྻܥ‬ϕΫτϧͰ͋Δ͔ΛධՁ͢Δ࣮‫͋Ͱݧ‬ΔɽධՁσʔλΛྫΛҎԼʹࣔ͢ɽ

S = {w1 , w2 ...wt } ΛͦΕͧΕͷೖྗ૚ʹೖྗ͢Δɽ௨ৗͷΤॻ͖‫ͨ͑׵‬ൺᄻจΛ‫ͯݟ‬จͷ಺༰͕ཧղՄೳͰ͋Δ͔Λ 3 ஈ֊ϯίʔμʔͷதؒ૚ f h ʹೖྗจΛ‫ʹ͖޲ٯ‬ೖྗ͢ΔΤϯίʔ(3: ཧղՄೳɼ2: จ຺ʹ͸ద͍ͯ͠ͳ͍͕ཧղՄೳɼ1: ཧղෆμʔͷதؒ૚ bh Λ࿈݁ͨ͠૒ํ޲Τϯίʔμʔ LSTM ͰจॻՄ) ͰධՁͨ͠ʢ஫ 10ʣɽΛม‫͢׵‬Δɽྫ) ൺᄻจཧղ༰қੑධՁσʔλͷྫf ht = LST Mf w (wt , ht−1 )(14)

bht = LST Mbw (wt , ht+1 )(15)

ht = [f ht ; bht ](16)

h = {h1 , ...ht }

(17)

•

ʮൠएͷΑ͏ͳ֎໘ʹ൓ͯ͠Թްͳੑ֨ʯ ධՁ஋ [3]•ʮ௨໷ͷΑ͏ͳ֎໘ʹ൓ͯ͠Թްͳੑ֨ʯ ධՁ஋ [1]ఏҊख๏ͷֶशʹ࢖༻͍ͯ͠ͳ͍ൺᄻ౓্Ґͷจ͔ΒϥϯμϜ
Ͱબ୒ͨ͠ 100 จʹର͠ɼ3 ਓͷධՁऀ͕ධՁͨ͠ɽ
ධՁ݁ՌΛද 3 ʹࣔ͢ɽॻ͖‫͑׵‬Λߦͬͨ 100 จͷฏ‫ۉ‬ධՁ֤୯‫ʹޠ‬ରԠ͢ΔӅΕ૚ ht Λೖྗͱ͠༧ଌͷࡍͦͷ୯‫ʹޠ‬஫஋Λ͍ࣔͯ͠Δɽൺᄻબ୒Ϟσϧͷ΄͏͕ൺᄻ౓ (P roposedM )໨͢΂͖֬཰Λ‫͢ࢉܭ‬Δɽ֤ӅΕ૚ͷঢ়ଶ ht ͸ൃ࿩શମͷ৘ͷΈͷ৔߹ΑΓΘ͔ͣʹߴ͍ධՁΛࣔͨ͠ɽ͔͠͠ɼͲͪΒͷखใΛ‫ؚ‬Έɼ֤୯‫ޠ‬ͷॏཁ౓Λߟྀͨ͠จͷද‫ݱ‬ϕΫτϧ αt Λ๏ͱ΋௿͍ධՁͰ͋ͬͨɽֶशʹ༻͍Δલͷൺᄻ౓ P roposedM

ग़ྗ͢ΔɽWa ,Ba ,ua ͸ϞσϧύϥϝʔλʔͰ͋Δɽ

ͷΈͷ৔߹Ͱ΋ฏ‫ۉ‬ධՁ஋͕௿͍ͨΊɼൺᄻ౓ࣗମͷվྑ͕ඞཁͰ͋Δɽ

ut = tanh(Wa ht + ba )(18)

exp(uTt ua )αt = 
exp(uTt ua )t(19)αt ht(20)α=5. 2 ஔ͖‫಺͑׵‬༰อ࣋ධՁࣗಈબ୒͞Εͨᄻ͕ࣙஔ͖‫ݩ͑׵‬ͷ಺༰Λอ͍࣋ͯ͠Δ͔ΛධՁ͢Δ࣮‫ݧ‬ΛߦͬͨɽධՁσʔλͷྫΛԼ‫ࣔ͢ʹه‬ɽஔ͖‫׵‬͑લͷจΛ‫ͯݟ‬ɼબ୒ͨ͠ൺᄻ͕‫ݩ‬ͷจͷ಺༰Λอ͍࣋ͯ͠Δ͔Ͳ͏͔Λ 2 ஋ (1: ಺༰Λอ͍࣋ͯ͠Δɼ0: ಺༰Λอ࣋Ͱ͖ͯ

tLSTM ͷߏ଄ʹΑΓɼೖྗͷશ࣌‫ྻܥ‬Λֶश͢Δɽͦͯ͠ද‫ݱ‬͍ͳ͍) ͰධՁͨ͠ɽ

ϕΫτϧ α Λ softmax ෼ྨ‫ʹث‬Αͬͯ෼ྨΛߦ͍ɼ֬཰஋͕ྫ) ஔ͖‫಺͑׵‬༰อ࣋ධՁσʔλɹ͍͔͍ͭ֎໘ʹ൓ͯ͠Թްͳੑ֨΋ͬͱ΋ߴ͍ᄻࣙ {m1 , m2 ...mn } Λબ୒͢Δɽ

5. ൺᄻબ୒ϞσϧධՁ࣮‫ݧ‬ൺᄻॻ͖‫͑׵‬Λߦ͏ʹ͋ͨͬͯॻ͖‫ͨ͑׵‬ൺᄻจ͕೔ຊ‫ͱޠ‬ͯ͠डཧͰ͖Δ͜ͱ͕࠷΋ॏཁͰ͋Δɽᄻࣙ͸Ұൠతʹஔ͖‫׵‬•ʮൠएͷΑ͏ͳʯ ධՁ஋ [1]•ʮ௨໷ͷΑ͏ͳʯ ධՁ஋ [0]ൺᄻબ୒Λߦͬͨจ͸ൺᄻจཧղ༰қੑධՁͱಉ͡ 100 จΛ࢖༻ͨ͠ɽ͑‫ͳͱݩ‬Δ‫ޠ‬ΑΓ΋ந৅తͳද‫͋Ͱݱ‬Γɼ༷ʑͳղऍ͕‫·ؚ‬ΕධՁ݁ՌΛද 4 ʹࣔ͢ɽ100 ݅தɼ಺༰Λอ͍࣋ͯ͠ΔൺᄻΔɽͦͷͨΊɼબ୒ͨ͠ᄻ͕ࣙඃᄻࣙΛम০͢Δ‫ͯ͠ͱޠ‬ద੾ද‫ݱ‬Λબ୒ͨ͠ਫ਼౓Λද͍ͯ͠Δɽൺᄻ౓ (P roposedM ) ͷΈͰͳ͍৔߹ɼ೔ຊ‫ͯ͠ͱޠ‬डཧͰ͖ͳ͍จʹॻ͖‫͏·ͯ͑͠׵‬ͷબ୒ख๏ͷ΄͏͕ධՁ͕ߴ͔ͬͨɽᄻࣙͱඃᄻࣙͷΧςΰϦ͜ͱʹͳΔɽͦ͜Ͱɼॻ͖‫ͨ͑׵‬จ͕ཧղՄೳͰ͋Δ͔ਓखධతྨࣅ౓΍ɼGoogle Ngram ʹΑΔᄻࣙͱඃᄻࣙͷ૊Έ߹ΘͤՁ࣮‫ݧ‬Λߦ͏ɽ·ͨɼൺᄻॻ͖‫͑׵‬Λߦ͏ʹ͋ͨͬͯஔ͖‫ݩ͑׵‬ͷ৘ใΛอ

ʢ஫ 10ʣɿ‫ݩ‬ͷද‫͕ݱ‬ԿͰ͔͋ͬͨ͸ඃ‫ऀݧ‬͸஌Βͳ͍͜ͱʹ஫ҙ- 39 -—5—ද4಺༰อ࣋ධՁ݁Ռ

બ୒ख๏

recall6. Ή ͢ ͼ

ൺᄻ౓ (P roposedM ) ͷΈ

0.31

ຊ‫Ͱڀݚ‬͸จ຺Λߟྀͨ͠ൺᄻจ΁ͷॻ͖‫͑׵‬Λߦ͏ͨΊɼNN ʹΑΔൺᄻબ୒Ϟσϧ

0.18ൺᄻબ୒Ϟσϧͷֶशʹ༻͍Δ௚ᄻੜ੒σʔληοτΛ࡞੒͠ൺᄻબ୒ΛߦͬͨɽਓखධՁ࣮‫͔ݧ‬ΒఏҊͨ͠ൺᄻબ୒ϞσϧΑΓɼൺᄻ౓ͷΈͷํ͕ΑΓจ຺ͷ಺༰Λอ࣋ͨ͠ॻ͖‫͑׵‬Λߦ͑ͨɽ͔͠͠ɼൺᄻબ୒ϞσϧΛ༻͍Δ৔߹࿈૝ྨ‫ࣙޠ‬యͰቚࡢࡼ࠺࡞͸࿈૝͞Εͳ͔ͬͨ৽‫ن‬ੑͷ͋Δൺᄻॻ͖‫͑׵‬Λߦ͏͜ͱ͕Մ

0.80ೳͰ͋Δɽࠓ‫ޙ‬ͷ՝୊ͱͯ͠ɼൺᄻ౓‫ࢉܭ‬ख๏ͷվྑ΍ௐ੔ɼ0.60ൺᄻબ୒ϞσϧͷվྑΛߦ͏ɽ

0.40

จ
0.200.00

ਤ 3 atteition ྫͷ࢖༻ස౓ͳͲɼਓؒͷ‫͍ۙʹ֮ײ‬είΞࢉग़ํ๏Λ༻͍Δൺᄻ౓ʹର͠ɼ࣌‫ྻܥ‬σʔλΑΓେ·͔ʹจ຺৘ใΛֶश͠ൺᄻબ୒Λߦ͏ൺᄻબ୒Ϟσϧͷ΄͏͕ஔ͖‫͑׵‬Δ‫ޠ‬ͷ಺༰Λอ͕࣋ࠔ೉Ͱ͋ΔɽΑͬͯɼධՁ݁Ռʹ͕ࠩੜ·Εͨͱߟ͑Δɽ

5. 3 ߟ࡯ࠓճͷൺᄻબ୒ϞσϧͰ͸ɼ಺༰Λอ࣋ͨ͠ൺᄻද‫͕ݱ‬બ୒͢Δ͜ͱ͕Ͱ͖ͳ͔ͬͨ‫ݪ‬Ҽͱͯ͠ɼAttention ͕ਖ਼͘͠‫ػ‬ೳ͍ͯ͠ͳ͍͜ͱ͕‫͛ڍ‬ΒΕΔɽਤ 3 ʹ Attention ͷྫΛࣔ͢ɽʮࠇ͍ߕႀͷ্ʹന૷ଋΛணͨஉʯ͕ʮࠇ༵ੴͷΑ͏ͳʯʹஔ͖‫͑׵‬Δ͜ͱ͕Ͱ͖ΔจΛೖྗͨ͠৔߹ɼAttention ͸ɼ΄΅ʮࠇ͍ʯͱ͍͏୯‫ޠ‬ͷΈʹ͔͠͞Ε͍ͯͳ͔ͬͨɽ͜ͷΑ͏ʹจ௕͕௕͍จͰ͸ஔ͖‫͑׵‬Δ৔ॴͷ෦෼͔͠ Attention ͕͞Ε͍ͯͳ͍ͨΊɼஔ͖‫ݩ͑׵‬΍ඃᄻࣙͷલ‫ޙ‬ͷ୯‫ޠ‬Λߟྀͤͣɼʮࠇ͍ʯʹஔ͖‫͑׵‬Δ͜ͱ͕ଟ͍ʮ๽ͷΑ͏ͳʯΛબ୒ͯ͠͠·͏ɽͦͷͨΊɼʮࠇ͍ʯ͕‫·ؚ‬ΕΔ΋ͷʹ͸ඞͣʮ๽ͷΑ͏ͳʯͱ͍͏ᄻ͕ࣙબ୒͞Ε͍ͯͨɽ͜ͷ͜ͱΑΓɼඃᄻࣙ΍ᄻࣙͷલ‫ޙ‬୯‫ޠ‬͸ಛʹ Attention Λ‫͘͢ڧ‬ΔͳͲͷվྑ͕ඞཁͰ͋Δʢ஫ 11ʣɽ

5.2 અͷධՁͰ͸ɼൺᄻ౓ͷΈΛ༻͍Δख๏ͷํ͕Α͍݁Ռ͕ͩͬͨɼNN Λ༻͍Δख๏ͷํ͕ධՁͷߴ͍ൺᄻॻ͖‫͑׵‬Λߦ͏ྫ΋ଘࡏͨ͠ɽਓखධՁ͕ߴ͔ͬͨൺᄻจʹʮ͖Ε͍ͳຍ࠿Λങͬͯ‫͖ͨͯͬؼ‬ʯΛʮ‫ݟ‬ຊͷΑ͏ͳຍ࠿Λങͬͯ‫ͯͬؼ‬͖ͨʯͱஔ͖‫ͨ͑׵‬จ͕͋Δɽ࿈૝ྨ‫ࣙޠ‬యͰ͸ʮ‫ݟ‬ຊͷΑ͏ͳʯ͸ʮ୅දతͳʯ΍ʮඪ४తͳʯͳͲదਖ਼ͳ΋ͷΛ࿈૝͢Δൺᄻද‫͞ͱݱ‬Ε͓ͯΓɼʮ͖Ε͍ͳʯͱ͍͏‫ܗ‬༰ࢺ͸औಘͰ͖ͳ͍ɽ͜ͷΑ͏ʹɼൺᄻ౓ͷΈͰͷॻ͖‫Ͱ͑׵‬͸બ୒Ͱ͖ͳ͍ൺᄻΛબ୒Ͱ͖ɼ৽‫ن‬ੑͷ͋Δൺᄻॻ͖‫͑׵‬Λߦ͏͜ͱ͕ՄೳͰ͋Δͱ͍͑Δɽ͕ͨͬͯ͠ɼΑΓਫ਼౓ͷߴ͍ൺᄻ౓ͷ‫ࢉܭ‬Λߦ͍ɼద੾ͳϖΞσʔλΛֶशͷͨΊͷ௚ᄻੜ੒σʔληοτͱ‫ݙ‬[1] George Lakoﬀ and Mark Johnson. Metaphors we live by.
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ͯ֫͠ಘ͢Δ͜ͱ͕ॏཁͰ͋Δɽʢ஫ 11ʣɿͨͩ͠ɼ࣮‫Ͱݧ‬͸ Attention Λ࢖༻͠ͳ͍ൺᄻબ୒ϞσϧͰ͸ͦ΋ͦ΋ద੾ͳϞσϧΛߏஙͰ͖ͳ͔ͬͨɽͦͷͨΊ Attention ͸ൺᄻબ୒Ϟσϧʹඞཁͳ‫͋Ͱߏػ‬Δɽ

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