Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
自然言語処理の基礎と応用 〜 料理と医療を題材として 〜 /JADI2021
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
j.harashima
September 10, 2021
5.6k
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
自然言語処理の基礎と応用 〜 料理と医療を題材として 〜 /JADI2021
j.harashima
September 10, 2021
More Decks by j.harashima
See All by j.harashima
日本語レシピデータセットの継続的な構築と複合的な利用/JED2022
junharashima
1
14k
企業での研究開発の楽しさと苦労/WAP-Tech-Talk
junharashima
1
12k
クックパッドにおける研究開発/HCG2020
junharashima
0
4.9k
Calorie Estimation in a Real-World Recipe Service/iaai-20
junharashima
0
15k
クックパッドと機械学習(短縮版)/MLTBP
junharashima
0
10k
クックパッドと NLP/CV/nlpaper-challenge
junharashima
0
150
AI 部門の実戦投入/repro-tech-meetup
junharashima
0
4.9k
クックパッドと機械学習/uec-aix-seminar
junharashima
0
12k
実データにふれる自然言語処理インターンシップ/internship2018
junharashima
1
16k
Featured
See All Featured
AI Search: Implications for SEO and How to Move Forward - #ShenzhenSEOConference
aleyda
1
1.3k
The untapped power of vector embeddings
frankvandijk
2
1.7k
How to Get Subject Matter Experts Bought In and Actively Contributing to SEO & PR Initiatives.
livdayseo
0
130
Visual Storytelling: How to be a Superhuman Communicator
reverentgeek
2
550
Agile that works and the tools we love
rasmusluckow
331
21k
Designing Experiences People Love
moore
143
24k
Marketing to machines
jonoalderson
1
5.4k
Effective software design: The role of men in debugging patriarchy in IT @ Voxxed Days AMS
baasie
0
390
A Soul's Torment
seathinner
6
2.9k
Conquering PDFs: document understanding beyond plain text
inesmontani
PRO
4
2.8k
Leveraging LLMs for student feedback in introductory data science courses - posit::conf(2025)
minecr
1
280
We Analyzed 250 Million AI Search Results: Here's What I Found
joshbly
1
1.3k
Transcript
ࣗવݴޠॲཧ ͷجૅͱԠ༻ ʙྉཧͱҩྍΛࡐͱͯ͠ʙ ݪౡ७
ژେֶʹͯࣗવݴޠॲཧͷݚڀࣨʹ ଐ ത࢜ʢใֶʣΛऔಘɺΫοΫύου ʹೖࣾ ݪౡ७ ݚڀ։ൃ෦Λ৽ઃ
ϨγϐݕࡧνʔϜΛ৽ઃɺϦʔμʔʹ बʢݚڀ։ൃ෦෦݉ʣ ಉ෦෦ʹब
໔ࣄ߲ ࠓͷൃදʮΩονϯɾΠϯϑΥϚςΟΫεʯͷ ༰Λϕʔεʹ͍ͯ͠·͢ɻ ͜ͷຊɺΫοΫύουͷࣾһͱͯ͠Ͱͳ͘ɺ ݸਓͱͯ͠ॻ͍ͨͷͰ͢ɻ ൃදʹؔ͢Δ࣭͝ɺձࣾʹͰͳ͘ɺ ݪౡݸਓʹ͓د͍ͤͩ͘͞ɻ
࣍ ࣗવݴޠॲཧͷجૅ ྉཧυϝΠϯʹ͓͚ΔԠ༻ ҩྍυϝΠϯʹ͓͚ΔԠ༻ ·ͱΊ
ࣗવݴޠॲཧͱʁ ࣗવݴޠʢʹݴ༿ʣΛίϯϐϡʔλͰѻ͏ͨΊͷٕज़ ࣗવݴޠࣗવൃੜతʹੜ·ΕͨݴޠʢFH ຊޠʣ ਓݴޠਓతʹ࡞ΒΕͨݴޠʢFH $ݴޠʣ
ใݕࡧʜϢʔβ͕ٻΊΔใΛจॻू߹͔Βݕࡧ ػց༁ʜҙຯΛอͬͨ··ɺ͋ΔݴޠͷจॻΛผͷݴޠͷจॻʹม จॻྨʜ༧ΊఆΊΒΕͨೋͭҎ্ͷΫϥεͷ͍ͣΕ͔ʢPS͍͔ͭ͘ʣʹจॻΛྨ จॻཁʜใΛอͬͨ··ɺҰͭʢPSෳʣͷจॻΛॖ ใநग़ʜจॻ͔ΒॏཁͳใΛநग़ FUD Ԡ༻ྫ ใݕࡧͷҰྫ
ػցֶशͱʁ ɾσʔλʹજΉύλʔϯΛػցతʹݟ͚ͭΔͨΊͷٕज़ ػցֶशʢಛʹχϡʔϥϧωοτϫʔΫʣʹΑΔ༷ʑͳൃల ɾ8PSE7FDʹΑΔҙຯͷϕΫτϧԽʢ.JLPMPWFUBM ʣ ɾ#&35ʹΑΔ֤छݴޠཧղλεΫͷੑೳվળʢ%FWMJOFUBM ʣ ۙɺଟ͘ͷاۀ͕ࣗવݴޠॲཧʹ ػցֶशͷ಄
ε ϙ ϯ α ồ ਓೳֶձશࠃେձ ը૾ͷೝࣝɾཧղγϯϙδϜ ݴޠॲཧֶձ࣍େձ ˞ʹεϙϯαʔ͕ݮ͍ͬͯΔͷίϩφͷӨڹ
ຊޠʹ͓͚Δࣗવݴޠॲཧ ୯ޠ୯Ґ จઅ୯Ґ จ୯Ґ Ԡ༻ʢFH ใݕࡧɺػց༁ʣ ߏจղੳ ܗଶૉղੳɹݻ༗දݱೝࣝ ड़ޠ߲ߏղੳɹڞࢀরղੳ ҙຯ୯Ґ
ಉٛؔೝࣝ ؚҙؔೝࣝ ຊൃදͰࣈͷٕज़ʹ͍ͭͯͷΈհʢͦͷଞʹ͍ͭͯஶΛ͝ཡ͍ͩ͘͞ʣ
ܗଶૉղੳ จதͷܗଶૉʢ㲈୯ޠʣͷڥքͱࢺɺݪܗΛೝࣝ͢Δॲཧ ຊޠ୯ޠ͕εϖʔεͰ۠ΒΕ͍ͯͳ͍ˠܗଶૉղੳຊޠॲཧͷୈҰา ϑϥϯε ଠ Λ ৯ ύϯ c
໊ࢺ ໊ࢺ ಈࢺ ॿࢺ ໊ࢺ c c c ৯Δ ଠ Λ ϑϥϯε ύϯ ݪܗ ڥք ࢺ c ॿࢺ ଠϑϥϯεύϯΛ৯ͨ ೖྗ ग़ྗ ͨ ͨ c ॿಈࢺ จ
ݻ༗දݱೝࣝ จதͷݻ༗දݱʢFH ਓ໊ɺ໊ɺʜʣΛೝࣝ͢Δॲཧ ܗଶૉղੳͷग़ྗΛೖྗͱ͠ɺݻ༗දݱͷϥϕϧΛग़ྗ ϑϥϯε ଠ Λ ৯ ύϯ
c ໊ࢺ ໊ࢺ ಈࢺ ॿࢺ ໊ࢺ c c c ৯Δ ଠ Λ ϑϥϯε ύϯ ݪܗ ڥք ࢺ c ॿࢺ ೖྗ ͨ ͨ c ॿಈࢺ ग़ྗ ਓ໊ ͦͷଞ ͦͷଞ ͦͷଞ ͦͷଞ ͦͷଞ ໊ ϥϕϧ
ಉٛؔೝࣝ ͋Δޠ۟ͱผͷޠ͕۟ಉ͡ҙຯ͔ҧ͏ҙຯ͔Λೝࣝ͢Δॲཧ ୯ޠಉ࢜Ͱಉ͡ҧ͏ҙຯͰɺผͷ୯ޠͱ߹ΘͤΔͱҧ͏ಉ͡ҙຯʹͳΔ͜ͱ ৯Δ ͨΔ ৯Δ ৯͏ ৯Δ ҿΉ ୯ޠϖΞ
ೖྗ ग़ྗ ೝࣝ݁Ռ ಉ͡ ҧ͏ ಉ͡ ৯͏ ͏ ҧ͏ ϑϨʔζϖΞ ೝࣝ݁Ռ ϝϞϦΛ৯͏ ϝϞϦΛ͏ ಉ͡ Ұഋ৯͏ Ұഋ৯Δ ҧ͏ ʢͩ·͞ΕΔʣ ೖྗ ग़ྗ
ࣗવݴޠॲཧͷπʔϧɾσʔλ .F$BCʢܗଶૉղੳثʣ ຊޠϫʔυωοτʢγιʔϥεʣ γιʔϥεʢྨޠࣙయʣ ྨޠኮදɺຊޠޠኮେܥɺʜ ੜίʔύε ੨ۭจݿɺ8JLJQFEJBɺʜ ऍ͖ίʔύε ژେֶςΩετίʔύεɺ/"*455FYU$PSQVTɺʜ ʜ
ܗଶૉղੳث 4VEBDIJɺ+6."/ʢ+6."/ ʣɺʜ ߏจղੳث ,/1ɺ$BCP$IBɺʜ ػցֶशϥΠϒϥϦ 1Z5PSDIɺ5FOTPS'MPXɺʜ ʜ
࣍ ࣗવݴޠॲཧͷجૅ ྉཧυϝΠϯʹ͓͚ΔԠ༻ ҩྍυϝΠϯʹ͓͚ΔԠ༻ ·ͱΊ
༷ʑͳՄೳੑ Ϩετϥϯ Ϩγϐ ϝχϡʔ ޱίϛ ใݕࡧ ػց༁ จॻྨ ʜ ྉཧυϝΠϯʹ͓͚ΔԠ༻
ʹ º ྉཧυϝΠϯʹ ͓͚ΔςΩετ ࣗવݴޠॲཧͷ Ԡ༻ ʜ ใநग़ ʢͷհϖʔδʣ
Ϩγϐºใݕࡧ ͨ·Ͷ͗ πφͱͨ·Ͷ͗ͷ ෩ύελ ߜΓࠐΉ ۄೢͱੜᇙͷ ͔͋ͬͨຯो
ΧϦΧϦ̇ ΦχΦϯϦϯά ۄͶ͗ͱτϚτͷ αϥμ ΦχΦϯάϥλϯ εʔϓ ܗଶૉղੳݻ༗දݱೝࣝΛ֤Ϩγϐʹద༻͠ɺ Ϩγϐதͷ୯ޠݻ༗දݱΛೝࣝ ֤୯ޠʹ͍ͭͯಉٛޠΛల։ FH ͨ·Ͷ͗ʹۄೢʹۄͶ͗ʹΦχΦϯ ΫΤϦʢ͓Αͼɺͦͷಉٛޠʣ͕ग़ݱ͢ΔϨγϐΛ ݕࡧ݁Ռͱͯ͠ఏࣔ
Ϩγϐºจॻྨ ͨ·Ͷ͗ ओࡊ ओ৯ ۄೢͱੜᇙͷ ͔͋ͬͨຯो ෭ࡊ ो ߜΓࠐΉ
ΦχΦϯάϥλϯ εʔϓ ཛͱ౾ͱۄೢͷ ༏͍͠εʔϓ ͨ·Ͷ͗ͱਓࢀͱ Ωϟϕπͷϙτϑ ܗଶૉղੳݻ༗දݱೝࣝΛ֤Ϩγϐʹద༻͠ɺ Ϩγϐதͷ୯ޠݻ༗දݱΛೝࣝ ֤୯ޠʹ͍ͭͯಉٛޠΛల։ FH ͨ·Ͷ͗ʹۄೢʹۄͶ͗ʹΦχΦϯ ֤ΫϥεʢFH ोʣʹॴଐ͖͢ϨγϐΛਓखͰ ऩू͠ɺͦΕΒʹग़ݱ͢Δ୯ޠͷύλʔϯΛѲ Ͱऩूͨ͠ϨγϐҎ֎ͷϨγϐʹύλʔϯΛద༻͠ɺ ֤Ϋϥεʹྨ
y1 yJ y2 … canonical form of the ingredient decoder
original string of an ingredient encoder x1 x2 xI … softmax softmax softmax … Ϩγϐதͷදه ৯දதͷදه දه༳Ε ਓࢀ ʹΜ͡Μ ུه ৽ͨ· ৽ͨ·Ͷ͗ εϖϧϛε ͋΅͕Ͳ ΞϘΧυ ͦͷଞ അླḕ ͡Ό͕͍ dense amount encoder xa1 xa2 xaJ … title encoder xt1 xt2 xtI … softmax y serving λΠτϧ .ࡼ /ਓ ίίφοπΦΠϧೖΓ΄͏ΕΜΧϨʔ ࡼ ͜ΜͿɾͻ͖͡ɾ΄ͨͯ͝Μ ߹ άϥϊʔϥΫοΩʔ ຕ ύϯϓΩϯλϧτ DNλϧτܕ ৄ͘͠ɺJun Harashima, Makoto Hiramatsu, and Satoshi Sanjo. Calorie Estimation in a Real-World Recipe Service. In Proceedings of the 32nd Annual Conference on Innovative Applications of Arti fi cial Intelligence (IAAI-20), 2020. Λ͝ཡ͍ͩ͘͞ɻ Ϩγϐºػց༁ʢΧϩϦʔܭࢉʣ Ϩγϐதͷ֤ࡐྉͷදهΛ৯දதͷදهʹม ʮ.ࡼʯʮ.߹ʯɺʮ.ຕʯͳͲΛʮ/ਓʯʹม ֤ࡐྉͷΧϩϦʔΛ৯ද͔Βऔಘ͠ɺ߹ܭ ߹ܭΛ/ͰׂͬͯɺਓͷΧϩϦʔΛऔಘ ˞ผݴޠͷ༁ʢҰൠతͳػց༁ʣͰͳ͘ɺಉҰݴޠͷ༁ʢݴ͍͑ʣ
࣍ ࣗવݴޠॲཧͷجૅ ྉཧυϝΠϯʹ͓͚ΔԠ༻ ҩྍυϝΠϯʹ͓͚ΔԠ༻ ·ͱΊ
༷ʑͳՄೳੑ Χϧς จ Ϩηϓτ αϚϦ ใݕࡧ ػց༁ จॻྨ ʜ ҩྍυϝΠϯʹ͓͚ΔԠ༻
ʹ º ҩྍυϝΠϯʹ ͓͚ΔςΩετ ࣗવݴޠॲཧͷ Ԡ༻ ʜ ใநग़
จºใݕࡧ Ϩγϐºใݕࡧͱಉ͡ϩδοΫʢͷͣʣ
Χϧςºใநग़ʢ͍ΘΏΔɺϏοάσʔλͷ׆༻ʣ ܦ95&$)ʮΧϧςղੳͰਫ਼ਆ࣬ױױऀͷ༧ޙΛ༧ଌʯ IUUQTYUFDIOJLLFJDPNENBUDMGFBUVSF
ҩྍºࣗવݴޠॲཧͷ͠͞ σʔλͷॴࡏ͕Θ͔Βͳ͍ ɾ·ͩੵ͞Εͯͳ͍ʁ ɾ͞Εͯͯɺར༻ͮ͠Β͍ʁ ɾσʔλ͕ͳ͍ͱɺࣗવݴޠॲཧແྗ ɹɹɹɹσʔλͷੵɾڞ༗͕ෆՄܽ σʔλͷ༰͕Θ͔Βͳ͍ ɾΠϯεϦϯʁϔϞάϩϏϯʁܕʁܕʁ ɾࣗવݴޠॲཧʢFH ใநग़ʣͷ݁Ռ͕ղऍෆೳ
ɾղऍ͕ෆೳͩͱɺվળෆೳ ɹɹɹɹҩྍͷϓϩͱͷ࿈ܞ͕ෆՄܽ ҩྍͷૉਓ͔Βݟͨ
࣍ ࣗવݴޠॲཧͷجૅ ྉཧυϝΠϯʹ͓͚ΔԠ༻ ҩྍυϝΠϯʹ͓͚ΔԠ༻ ·ͱΊ
ࣗવݴޠॲཧͷجૅͱԠ༻ʙྉཧͱҩྍΛࡐͱͯ͠ʙ ࣗવݴޠॲཧͱʁ ɾࣗવݴޠʢʹݴ༿ʣΛίϯϐϡʔλͰѻ͏ͨΊͷٕज़ ɾ͜͜Ͱٸܹʹൃలɺଟ͘ͷاۀ͕ ςΩετσʔλ͑͋͞Εɺ༷ʑͳԠ༻͕ߟ͑ΒΕΔ ɾྉཧυϝΠϯʹ͓͚ΔςΩετºࣗવݴޠॲཧͷԠ༻ྉཧυϝΠϯʹ͓͚ΔԠ༻ ɾҩྍυϝΠϯʹ͓͚ΔςΩετºࣗવݴޠॲཧͷԠ༻ҩྍυϝΠϯʹ͓͚ΔԠ༻ ҩྍºࣗવݴޠॲཧͷ͠͞ ɾσʔλͷॴࡏ͕Θ͔Βͳ͍ˠσʔλͷੵɾڞ༗͕ෆՄܽ ɾσʔλͷ༰͕Θ͔Βͳ͍ˠҩྍͷϓϩͱͷ࿈ܞ͕ෆՄܽ