A Symptom Recognition Method of Diseases for Senior User Based on Language Model

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[email protected], {jwchoi, tkwhagbo}@gachon.ac.kr

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A Symptom Recognition Method of Diseases

for Senior User Based on Language Model

Min-Kyung Park*, Jin-Woo Choi*, Taeg-Keun Whangbo**†

*A.I. Healthcare Research Center, Gachon University **Dept. of Computer Engineering, Gachon University

殚 檃檃 2025⎚ 㽞ἶ⪏ ㌂䣢⪲ 㰚㧛䞶 ộ㦒⪲ 㡞㌗♾㠦 ➆⧒ ἶ⪏䢪 㔲╖㠦 ⹲㌳䞮⓪ ⶎ㩲㩦✺㦚 IT₆㑶㦚 㦧㣿䞮㡂 㰖⓻㩗㦒⪲ 䟊ἆ䞶 㑮 㧞⓪ 㧎Ὃ㰖⓻ 䡂㓺䅖㠊 ㏪⬾㎮㧊 㭒⳿⹱ἶ 㧞┺. BISⰂ㍲䂮㦮 ⽊ἶ㍲㠦 ➆⯊Ⳋ 䡂㓺䅖㠊 ㌆㠛㦮 㺭⽝ 㔲㧻 ′⳾Ṗ 2029⎚ 㟓 4㠋 9,800Ⱒ ╂⩂⪲ ㎇㧻䞶 ộ㦒⪲ 㡞㌗♲┺. ➆⧒㍲ 㔲┞㠊 ㌂㣿㧦⯒ 㥚䞲 ₆㑶 㡆ῂṖ 㩗⁏㩗㦒⪲ 䞚㣪䞲 㔲㩦㧊┺. ⽎ ⏒ⶎ㠦㍲⓪ ㌂㩚䞯㔋䞲 㠎㠊⳾◎ὒ BiLSTM₆⹮ 㔶ἓⰳ ⳾◎㦚 㧊㣿䞮㡂 㔲┞㠊 ㌂㣿㧦㠦Ợ 䔏䢪♲ 㰞䢮 㯳㌗ 㧎㔳 ⳾◎ ῂ䡚㠦 ὖ䞲 ⻪㥚 ⹥ ⹿⻫㠦 ὖ䟊 ₆㑶䞲┺. 㧊⓪ 㔲┞㠊 ╖㌗ ỊṫὖⰂ 㺭⽝ ㏪⬾㎮㠦 ☚㧛䞮㡂 㔲┞㠊 ㌂㣿㧦㠦Ợ 㧦㭒 ⹲㌳䞮⓪ 㰞䢮✺㦚 㫆₆㠦 ⹲ἂ䞶 㑮 㧞☚⪳ 㰖㤦䞮㡂 㥚䠮㦮 ⹲㌳ 㡞⹿㠦 ☚㤖㦚 㭒⓪ ㍲゚㓺Ṗ ♶ ộ㦒⪲ 㩚ⰳ䞲┺. 1. 昢嵦 䐋Ἒ㼃㦮 2019 ἶ⪏㧦 䐋Ἒ㠦 ➆⯊Ⳋ, 㤆Ⰲ⋮⧒ 65㎎ 㧊㌗ ἶ⪏ 㧎ῂ⓪  ㏣䧞 㯳Ṗ䞮㡂, 2019⎚㠦⓪ ἶ⪏㌂䣢Ṗ ♮㠞㦒Ⳇ, 2025⎚㦖 㽞ἶ⪏ ㌂䣢⪲ 㰚㧛䞶 ộ㦒⪲ 㩚ⰳ♮ἶ 㧞┺. 㧊㠦 ➆⧒ 㧎Ὃ㰖⓻ 䡂㓺䅖㠊 ㏪⬾㎮㧊 㧎ῂ ἶ⪏䢪㢖 Ⱒ㎇㰞䢮 䢮㧦  㯳㠦 ➆⯎ ㌌㦮 㰞 㩖䞮㠦 ╖䞲 ㍶㩲㩗, 㡞⹿㩗 ╖㦧㦮 䟋㕂₆㑶⪲ 㭒⳿⹱ἶ 㧞┺. 䔏䧞 㧎Ὃ㰖⓻ ₆㑶ὒ 㧦㡆㠊㻮Ⰲ ₆㑶㦚 㩧⳿䞲 ‘㺭⽝’ ₆㑶㦖 ㌂㣿㧦㢖 ╖䢪⯒ ⋮⑚Ⳇ ┺㟧䞲 㦮䞯㩗 㣪ῂ⯒ 䟊ἆ䞶 㑮 㧞⓪ 㺓ῂ㦮 ₆⓻㦚 䞶 ộ㦒⪲ ₆╖♮ἶ 㧞┺. 䞮㰖Ⱒ 㞚㰗 㺭⽝㦖 ㌂㣿㧦㦮 㣪㼃ὒ 㦮☚㦮 ⒮㞯㓺⯒ 㢚⼓䧞 㧊䟊䞮㰖⓪ ⴑ䞮Ⳇ 㦧╋㠦 ὖ䞲 㧦㥶☚Ṗ ⌄┺⓪ 䞲ἚṖ 㧞┺. 㧒⹮ ㌂㣿㧦Ṗ ㌂㣿䞮₆㠦☚ 䞲ἚṖ 㧞⓪ 㺭⽝㦚 㔲┞㠊 ㌂㣿㧦㠦Ợ 㩗㣿䞮⩺ἶ 䞲┺Ⳋ ┺⯎ 㩧⁒ ⹿㔳㦚 㧊㣿䟊㟒 䞲┺ἶ 䕦┾䞮㡖┺. ➆⧒㍲ ⽎ 㡆ῂ㠦㍲⓪ ₆㫊㦮 ╖䢪㔳 ⳛ⪏㦚 㧊㣿䞮⓪ 㺭⽝㧊 㞚┢ 㔲┞㠊 ㌂㣿㧦㦮 ⹲䢪⯒ 䐋䟊 㯳㌗㦚 㡞䁷䞮ἶ ⋮㞚Ṗ ὖ⩾♲ 㰞⼧㠦 ╖䞲 㩫⽊⯒ ⹱㦚 㑮 㧞⓪ ‘㔲┞㠊 Ịṫ ὖⰂ’ ⿚㟒㠦 㰧㭧䞲 ❻⩂┳ὒ 㧦㡆㠊㻮Ⰲ ₆⹮㦮 ‘㔲┞㠊 ╖㌗ ỊṫὖⰂ 㺭⽝ ㏪⬾㎮’ Ṳ⹲ ⌊㣿 㭧 ‘㔲┞㠊 ㌂㣿㧦 㰞䢮 㯳㌗ 㧎㔳’ 㔶ἓⰳ ⳾◎㦚 Ṳ⹲䞮㡖ἶ 㧊⯒ ₆㑶䞲┺. 2. 微塾 割笊 憚氊 愕 愯憛 ࣮࣭࣪ࣜ檾檺ࣜ微塾ࣜ穟枻ࣜ塶決瘶ࣜ ╖㣿⨟㦮 䆪䗒㓺⯒ ㌂㩚 䞯㔋㔲䅲 ┾㠊 㧚⻶❿㦚 ㌳㎇䞮⓪ 㠎㠊⳾◎㦚 ῂ㿫䞮₆ 㥚䟊, ‘⍺㧊⻚ 㰖㔳㧎’㦮 Ịṫ 䃊䎢ἶⰂ 㰞ⶎ ⁖㦚 ►䝚 ◆㧊䎆⪲ ㌂㣿䞮㡖┺. 䡂㓺䅖㠊 ⿚㟒⓪ 㺭⽝㧊 㦧╋㦚 㧦㡆㓺⩓Ợ 䞮⩺Ⳋ ⹿╖䞲 㰖㔳ὒ 䞯㔋㧊 㣪ῂ♮⓪◆, 㔺㩲 㦮⬢◆㧊䎆⓪ 㩧⁒㠦 䞲ἚṖ 㧞┺. Ṳ⹿䡫 ◆㧊䎆⋮ ㍲゚㓺 ⡦⓪ 䋂⪺Ⱇ ₆㑶㦚 䐋䟊 䢲㣿 Ṗ⓻䞲 䆮䎦䁶 䢫⽊⯒ 㥚䟊 ‘⍺㧊⻚ 㰖㔳㧎’㦮 Ịṫ 䃊䎢ἶⰂ 㰞ⶎ ⁖ 㭧 ‘㦮㌂ ╋⼖’㧊 ❇⪳♲ Ợ㔲⁖㦚

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䋂⪺Ⱇ䞮㡂 㺭⽝㠦 䢲㣿䞶 㑮 㧞⓪ 䡫䌲⪲ ῂ㿫䞮㡖┺. ⽊䐋 㧦㡆㠊㻮Ⰲ⯒ 㥚䞲 䆪䗒㓺 ῂ㿫㠦⓪ Wikipediaṯ㦖 ⶎ㠊㼊⪲ 㧊⬾㠊㰚 ►䝚 ◆㧊䎆⯒ Ⱔ㧊 ㌂㣿䞮㰖Ⱒ, ╖䢪㼊⪲ 㧧㎇♲ ‘⍺㧊⻚ 㰖㔳㧎’ ◆㧊䎆Ṗ 㺭⽝ ⹲䢪 ⹥ 㦧╋ 㔲㓺䎲㦚 ῂ㿫䞮₆㠦 㩗㩞䞶 ộ㦒⪲ 䕦┾䞮㡖┺. 䞲 ⶎ㧻㝿 ◆㧊䎆 ㎡㦚 ῂ㎇䞮Ⳋ ⶎ㧻╏ 䏶䋆㦮 㑮Ṗ 㧧㦒⸖⪲ ⳾◎㧊 ⶎⰻ㦚 䕢㞛䞮₆ 䧮✺㠊 䞯㔋㧊 㧮 㰚䟟♮㰖 㞠⓪┺. ➆⧒㍲ 䞲 Ợ㔲 ⁖ ╏ 䞮⋮㦮 ◆㧊䎆⪲ ῂ㎇䞮㡖㦒Ⳇ Word2Vec ⳾◎ 䤞⩾ ◆㧊䎆⪲ ㌂㣿䞲 Ợ㔲⁖㦖 㽳 175,884Ṳ㧊┺. ࣮ ࣮࣮࣪ࣜ滎筞ࣜ溣旇ࣜ穟枻ࣜ塶決瘶ࣜ Ṗ㻲╖ ₎⼧㤦 㦮⬢㰚✺㦚 ╖㌗㦒⪲ ㍺ⶎ㫆㌂ ⹥ 㰚⬢䡧⩻㎒䎆 㧦⬢⯒ 䐋䟊 䢫⽊䞲 ‘㰞䢮DB ◆㧊䎆’⓪ 㔲┞㠊 ┺ゞ☚ 㰞䢮㠦 ὖ⩾♲ 㯳㌗ⳛ 36Ṗ㰖㦮 ‘㯳㌗ⳛ’, ‘㩫㦮’, ‘☯㦮㠊’Ṗ 㑮⪳♮㠊 㧞┺. 㧊 㭧 ‘㯳㌗ⳛ’ὒ ‘☯㦮㠊’⯒ 䋺㤢✲⪲ ㌂㣿䟊 㰖㔳㧎 ◆㧊䎆(2009-2020)⯒ 㿪㿲䞮㡖┺. Supervised Learning㦚 㰚䟟䞮₆ 㥚䟊 㿪㿲䞲 Ợ㔲 ⁖ 㭧 Question 䅂⩒㦖 㧛⩻◆㧊䎆⪲ Keyword 䅂⩒㦖 Label⪲ ㌂㣿䞮㡖┺. <䚲1> 䤞⩾ ◆㧊䎆 㡞㔲 ῂ⿚ ⌊㣿 Index 4 Question Ⳇ䂶 㩚⿖䎆 㕂㧻㧊 Ⱎ䂮 ₊㧻♮Ệ⋮ ⟾ Ⰲ❅ 䞲 ⓦ⋢ὒ Ṗ㔊㧊 䎆㰞 ộ ṯ㦖 䐋 㯳ὒ 䞾℮ 㔳㦖➖☚ ⋮ἶ 10 ⿚ Ṗ₢㧊 㑾㧊 _{㫆㡂 㡺⓪ ❅ 䟞㔋┞┺.} Keyword 㕂Ἒ䟃㰚 KoNLPy㦮 ‘Kkma’ 䡫䌲㏢ ⿚㍳₆⯒ ㌂㣿䟊 ◆㧊䎆⯒ 㩫㩲䞮㡖ἶ, 䞲 ⶎ㧻 ⌊ 䏶䋆 㑮⓪ ╖⿖⿚ 500㞞䕤㧊Ⳇ 㭒⪲ 0-200 ㌂㧊㧎 ộ㦚 ἶ⩺䟊 ₎㧊⯒ 512⪲ 㩲䞲䞮㡂 Outlier⯒ ⶊ㔲䞮㡖┺. 㧊⩝Ợ 㽳 115,711Ṳ㦮 䤞⩾◆㧊䎆⯒ ㌳㎇䞮㡖┺. ࣮࣯࣪ࣜ檾檺ࣜ微塾ࣜ 㠎㠊⳾◎ ㌳㎇㦚 㥚䟊 䎣㓺䔎 ⿚⮮ ⶎ㩲㠦 Ⱔ㧊 ㌂㣿䞮⓪ 㧎Ὃ 㔶ἓⰳ ₆⹮㦮 䎣㓺䔎 㧚⻶❿ ⹿⻫⪶ 㭧 䞮⋮㧎 Word2vec㦚 㧊㣿䞮㡖┺. Word2vec㦖 㥶㌂䞲 㦮⹎Ṗ 㧞⓪ 㠊䥮⓪ 㥶㌂䞲 ⶎⰻ㠦㍲ ❇㧻䞲┺⓪ Distributional Hypothesis㠦 ₆⹮䞮㡂 㧎Ὃ 㔶ἓⰳ㦚 㧊㣿䟊 ┾㠊(䏶䋆)⯒ 㡆㏣㩗㧎 ⻷䎆 ὋṚ㦒⪲ 㧚⻶❿䞮⓪ ⹿⻫㧊┺. ’ሺ‘ȁ…ሻ ൌ ‡š’ሺݑ௢ ்_ݒ ௖ሻ ෌ ‡š’ሺݑ௪்ݒ௖ሻ ௐ ௪ୀଵ (㔳1) Distributional Hypothesis Word2vec㦖 㭧㕂┾㠊(c)Ṗ 㭒㠊㪢㦚 ➢, 㭒⼖┾㠊(o)Ṗ ❇㧻䞶 㫆Ị⿖ 䢫⮶㧎 (㔳1)㦚 㾲╖䢪䞮⓪ 㴓㦒⪲ 䞯㔋㧊 㰚䟟♲┺. 㽳 175,884Ṳ㦮 ◆㧊䎆⯒ KoNLPy㦮 ‘Kkma’ 䡫䌲㏢ ⿚㍳₆⯒ ㌂㣿䟊 㿪㿲䞲 䏶䋆㦮 㑮⓪ 㽳 30,830Ṳ 㡖㦒Ⳇ Word2vec ⳾◎㦮 Hyper parameters⓪ ┺㦢 <䚲2>㢖 ṯ┺.

<䚲2> Word2Vec Model Hyper Parameters

,WHU 6L]H :LQGRZ :RUNHUV 0LQBFRXQW 6J       Size⓪ 㧚⻶❿ ⻷䎆㦮 㹾㤦㦚 ⦑䞮⓪◆, 㹾㤦㧊 ⏨㦚㑮⪳ Embedding word㦮 䛞㰞㧊 䟻㌗♮ἶ 㭒⪲ 128, 256㹾㤦㦒⪲ 㰖㩫䞲┺.[1] 㔺䠮㠦㍲⓪ 200㹾㤦㠦㍲ 300㹾㤦 ㌂㧊⪲ 㰖㩫䞶 ➢ 㫡㦖 ㎇⓻㦚 ⽊㡖┺. min_count⓪ ┾㠊 ❇㧻 㾲㏢ ゞ☚㑮⯒ 㦮⹎䞮Ⳇ 3㠦㍲ 5, 10㦒⪲ ⓮⩺ṖⳆ 㔺䠮㦚 㰚䟟䞮㡖㦒Ⳇ min_count㦮 㑮Ṗ ⏨㞚㰞㑮⪳ Task Model㦮 val_loss Ṩ㧊 Ṳ㍶♮㠞┺. Sg⓪ 0㧊Ⳋ CBOW, 1㧊Ⳋ Skip-grams ⹿㔳㦚 ㌂㣿䞲┺. 㡂⩂ ⏒ⶎ㠦㍲ ㎇⓻ ゚ᾦ⯒ 㰚䟟䞮㡖㦚 ➢, 㩚⹮㩗㦒⪲ Skip-grams ㎇⓻㧊 㫡┺ἶ 㞢⩺㪎 㧞┺[1]. ⡦, Window⓪ 10㦒⪲ ㍺㩫䞮㡖ἶ 㧊⓪ Skip-grams⯒ ㌂㣿䞶 ἓ㤆 ῢ㧻 Ṩ㧊┺[2]. ┺⯎ 䕢⧒⹎䎆⓪ Task Model㦮 ㎇⓻㠦 ⑞㠦 ⦚⓪ 㡗䟻㧊 㠜㠞┺. ࣰ࣮࣪ࣜ柦凃廣ࣜ穟枻ࣜ微塾ࣜ RNN㦮 Gradient vanishing/exploding ⶎ㩲㠦㍲㦮 䀾㟓㩦㦚 Ṳ㍶䞲 ‘Bidirectional LSTM’⳾◎㦚 ⿚⮮ ⳾◎⪲ ㌂㣿䞮㡖┺. BiLSTM(㟧⹿䟻 㧻┾₆ ₆㠋)Ἒ䂋㦖 㔲Ἒ㡊, 㔲䉖㓺 ◆㧊䎆㦮 㓺䎳 Ṛ㦮 㟧⹿䟻 㧻₆ 㫛㏣㎇㦚 䞯㔋䞮ἶ ⳾◎㧊 㩚㼊 㔲䉖㓺⪲⿖䎆 䞯㔋䞮☚⪳ 䞶 ➢㠦 㥶㣿䞮⸖⪲ ⳾◎⪲ ㍶㩫䞮Ợ ♮㠞┺[3]. ₊ ⶎ㧻㠦㍲ 䙂䞾䞲 ┾㠊㦮 㭒⼖ 㩫⽊⯒ ‶䡫 㧞Ợ ╊₆ 㥚䞲 BiLSTM ⩞㧊㠊㢖 ṗ Feature Map㦮 ㌗㦮 ⏎✲㦮 䘟‶Ṩ㦚 ㆧ㞚 㹾㤦㦚 㭚㧊⓪ GlobalMaxPool1D⩞㧊㠊⪲ ⳾◎㦚 ῂ㎇䞮㡖┺. ⳾◎ 㿲⩻䂋㦮 䢲㎇䢪(activation), ㏦㔺(loss) 䞾㑮⓪ Multi-class Classification ⶎ㩲㠦 ㌂㣿䞮⓪ ‘Softmax’, ‘Categorical Cross-entropy’⪲ Keras API⯒ ㌂㣿䞮㡖ἶ batch䋂₆⓪ 64⪲ 䞯㔋䞮㡖┺. ὒ㩗䞿㦚 ⹿㰖䞮₆ 㥚䟊 Keras API EarlyStopping㦚 loss₆㭖㦒⪲ 㩗㣿䞮㡖ἶ epoch 40㠦㍲ 䤞⩾㦚 Ⲟ㿪㠊 ⽋㧷☚⯒ ⌄Ợ 䕢⧒⹎䎆⯒ 㫆㩫䞮㡖ἶ 㻮㦢 㰖㩫䞲 epoch 300 ⳾⚦ 㑮䟟䞲 䤚 䤞⩾㦚 Ⱎ㼺┺.

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࣯

࣯࣪ 冶刂ࣜ愕ࣜ把昣ࣜ

࣯࣭࣪ 穟枻ࣨࣜ円溣ࣜ愕ࣜ柢竞ࣜ塶決瘶ࣜ冶刂ࣜ

<䚲3> Model Performance Evaluation

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<䚲4> Classification Report

 3UHFLVLRQ 5HFDOO )VFRUH 0DFURDYJ    :HLJKWHGDYJ    ⳾✶ 䋊⧮㓺㦮 䘟‶ Precision, Recall, F1-score⓪ <䚲4>㢖 ṯ㦒Ⳇ ṗ 䋊⧮㓺㦮 㑮⯒ ἶ⩺䞮㰖 㞠⓪ Macro 䘟‶ ⽊┺, 䋊⧮㓺 㑮⼚ Ṗ㭧䂮⯒ ⚪ Weighted 䘟‶㦮 Ṩ㧊 ▪ 䅎┺. ṗ 䋊⧮㓺㦮 f1-score, recall, precision㦚 ἶ⩺䞮㡂 㾲㫛 ⳾◎㦚 ㍶㩫䞮㡖┺. ⽎ ⶎ㩲⓪ ┺㭧 䋊⧮㓺 ⶎ㩲⯒ ┺⬾ἶ 㧞㠊 Accuracy ㎇⓻⽊┺⓪ Precision, Recall㦮 Ṗ㭧㫆䢪䘟‶(Weight harmonic Average)㧎 F-score⯒ 㰖䚲⪲ 䢲㣿䟊㟒 䞲┺. Scikit-learn㦮 metrics 䕾䋺㰖⯒ 䐋䟊 ṗ 䋊⧮㓺⼚ precision, recall, f1-score⯒ 䙂὚㩗㦒⪲ ㌊䘊⽊㞮ἶ 䤞⩾◆㧊䎆⓪ ṗ class㦮 Ṳ㑮Ṗ ┺⯎ Imbalanced ◆㧊䎆⸖⪲ f1-score㠦 㭧㩦㦚 ⚦㠊 㾲㫛 ⳾◎㦚 ㍶㩫䞮㡖┺. ࣯࣮࣪ 冶刂 把昣 㔺䠮 ἆὒ⓪ 䏶䋆㦮 㑮Ṗ Ⱔ㦖 ⶎ㧻㦒⪲ 䞯㔋䞮㡂☚ 㩗㦖 䏶䋆㦮 㑮㦮 ⹲䢪 ◆㧊䎆☚ 㧮 㧎㔳䞲┺⓪ ộ㦚 䢫㧎䞮㡖┺⓪ ộ㠦 㦮㦮Ṗ 㧞┺. 䤞⩾◆㧊䎆⓪ 䘟‶ 70Ṳ㦮 䏶䋆㦒⪲ 㤆ⰂṖ 㺭⽝㠦 ㌂㣿䞮⓪ ◆㧊䎆㢖⓪ ┺㏢ 㹾㧊Ṗ 㧞┺. 㺭⽝ ⹲䢪㠦 ㌂㣿♮⓪ ⶎ㧻㦖 3~20Ṳ 㩫☚㦮 䏶䋆㦚 Ṗ㰚 ₎㧊㦮 ⶎ㧻㧒 ộ㧊┺. ㍶㩫䞲 ⳾◎⪲ 㡞䁷 ⳾✞㦚 Ⱒ✺ἶ, ‘㣪㯮㠦 㧼㦖 ₆䂾㧊 㧞㠊.’, ‘㠊㩲⓪ 㧝ⴎ㠦㍲ 䞒Ṗ ⌂㠊.’ ❇ 㔺㩲 㺭⽝㠦 ㌂㣿䞮❅ ⶎ㧻㦚 㧛⩻㦒⪲ ⍹ἶ 䎢㓺䔎⯒ 㰚䟟䟊 ⽊㞮㦚 ➢, ₊ ⶎ㧻㦒⪲ 䎢㓺䔎䟞㦚 ➢㢖 䋆 㹾㧊 㠜㧊 㧮 㰚䟟♮㠞┺. ⡦䞲 36Ṳ㦮 䋊⧮㓺 㭧 24Ṳ㦮 䋊⧮㓺㦮 f1-scoreṖ 0.9㧊㌗㦒⪲ 䁷㩫♮㠞┺. 䞮㰖Ⱒ, ◆㧊䎆⯒ 㑮㰧䞮⓪ ὒ㩫㠦㍲ 䋊⧮㓺 ⿞‶䡫㧊 ㌳₆ἶ 㔺㩲⪲ 䎢㓺䔎⯒ 㰚䟟䟊 ⽊㞮㦚 ➢☚ 㑮Ṗ 㩗㦖 䋊⧮㓺㦮 㡞䁷㦖 ㎇⓻㧊 ┺⯎ 䋊⧮㓺㠦 ゚䟊 ⟾㠊㰚┺⓪ 㡆ῂ[4]㻮⩒ 㔺㩲 䎢㓺䔎㠦㍲☚ 㧊⯒ 䢫㧎䞮㡖┺. ➆⧒㍲ 䟻䤚 㡆ῂ⪲ 㨂㌮䝢Ⱇ ₆⻫㦚 㧊㣿䞮㡂 㧊⯒ ⽊㢚䞮Ệ⋮ ◆㧊䎆 ῂ㿫㧊 Ṗ⓻䞮┺Ⳋ, ⶎ㩲 㧦㼊⯒ Multi-Label Classification ⶎ㩲⪲ ⹪∎㠊 㡆ῂ⯒ 㰚䟟䞮⩺ἶ 䞲┺. ࣰ࣪ 冶嵦ࣜ ⽎ 㡆ῂ㠦㍲⓪ ‘㔲┞㠊 ╖㌗ ỊṫὖⰂ 㺭⽝ ㏪⬾㎮’㦚 㥚䟊 㔲┞㠊 ㌂㣿㧦㦮 ⹲䢪 ◆㧊䎆⯒ 㠑ἶ, ⹲䢪 ㏣㦮 㯳㌗㦚 ⿚⮮䞮⓪ 㔶ἓⰳ ⳾◎ 㡆ῂ㠦 ὖ䟊 ₆㑶䞮㡖┺. 㡆ῂ⯒ 䐋䟊 㾲㫛㩗㦒⪲ ㍶㩫♲ Word2vec 㠎㠊 ⳾◎ὒ BiLSTM 㔶ἓⰳ ⳾◎㦮 㫆䞿㦒⪲ ῂ㎇♲ 㯳㌗ ⿚⮮ ⳾◎㦮 䢲㣿⹿㞞㦖 ┺㦢ὒ ṯ┺. Ịṫ⽊䠮Ὃ┾ 㧦⬢⯒ ⹪䌫㦒⪲ 㔲┞㠊 ┺ゞ☚ 㰞䢮 ㌗㥚 100Ṳ 㭧 㰞䢮ⳛ㧊 䔏㩫♮⓪ 㰞䢮✺ 41Ṗ㰖⯒ ㍶㩫䞮ἶ 㰞䢮 ⼚ ‘㩫㦮’, ‘㤦㧎’, ‘㰚⬢ὒ’, ‘㰚┾’, ‘㯳㌗’, ‘㯳㌗ ㍺ⳛ’, ‘䂮⬢’, ‘☯㦮㠊’, ‘ὖ⩾ 㰞䢮’ 䅂⩒㦒⪲ ῂ㎇♲ 䢲㣿◆㧊䎆⯒ 㧊㣿䞮㡂, 㔲┞㠊 ㌂㣿㧦㦮 ⹲䢪㠦㍲ ⿚⮮♲ 㯳㌗✺㦮 㫆䞿㦒⪲ 㔲┞㠊 ㌂㣿㧦✺㠦Ợ 㦮㕂♮⓪ 㰞䢮㦚 㞢⩺㭒ἶ 㰞䢮㦮 㩫⽊㠦 ὖ䟊 㞢⩺㭒㠊 ỊṫὖⰂ⯒ ☚㢖㭒⓪ 䡂㓺䅖㠊 㺭⽝㦚 ῂ䡚䞶 㡞㩫㧊┺. 㧊⓪ AI₆㑶ὒ ㌂䣢㩗 㧊㓞Ṗ 㦋䞿♲ 㔶㎇㧻 ☯⩻ ㍲゚㓺⪲㖾 㫡㦖 㡞Ṗ ♶ ộ㧊Ⳇ 㔲┞㠊 ㌂㣿㧦✺㦮 ┺㟧䞲 㦮䞯㩗 㣪ῂ⯒ 䟊㏢䞮Ⳇ ⋮㞚Ṗ㍲⓪ 㦮⬢゚ Ṧ㏢, ⳾┞䎆Ⱇ 㔲㓺䎲 ╖㼊⪲ 㧎Ị゚ ❇㠦 ╖䟊 ゚㣿 㩞Ṧ㧊 Ṗ⓻䞲 ₆㑶㧊 ♶ ộ㦒⪲ ₆╖♲┺. Acknowledgement ⽎ 㡆ῂ⓪ ἓ₆☚㦮 ἓ₆☚ 㰖㡃䡧⩻㡆ῂ㎒䎆 ㌂㠛㦮 㧒䢮㦒⪲ 㑮䟟䞮㡖㦢. [GRRC-Ṗ㻲2017(B04), 㧎Ὃ㰖⓻₆⹮ 㦮⬢㌗╊ 㺭⽝ 㾲㩗䢪 ㏪⬾㎮ Ṳ⹲] 焾処怾竒

[1] Mikolov, Tomas; et al, ICLR 2013 conference submission, "Efficient Estimation of Word Representations in Vector Space". arxiv.org/abs/1301.3781, 2013

[2] "Google Code Archive - Long-term storage for Google Code Project Hosting",

https://code.google.com/archive/p/word2vec/

[3] Tao Chen; et al. “Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN”, Elsevier, 2017 [4] ㍲⹒㰖 㣎, 䋊⧮㓺 ⿞‶䡫 ⶎ㩲Ṗ 㧞⓪ ┺㭧䋊⧮㓺 䎣㓺䔎 ⿚⮮㠦㍲㦮 䔏㰫 ㍶䌳 ⹿⻫. ╖䞲㌆㠛Ὃ䞯䣢㰖, 45(2), 93-100, 2019

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