Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
専門用語抽出手法の研究と 抽出アプリケーションの開発
Search
Koga Kobayashi
September 27, 2018
Programming
1.3k
1
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
専門用語抽出手法の研究と 抽出アプリケーションの開発
Koga Kobayashi
September 27, 2018
More Decks by Koga Kobayashi
See All by Koga Kobayashi
第13回 Data-Centric AI勉強会, LLMのファインチューニングデータ
kajyuuen
4
1.9k
基礎数学の公式
kajyuuen
1
190
初等確率論の基礎
kajyuuen
1
210
Deep Markov Model を数式で追う (+ Pyroでの追試)
kajyuuen
0
960
Fundamentals of Music Processing (Chapter 5)
kajyuuen
0
110
完全なアノテーションが得られない状況下での固有表現抽出
kajyuuen
3
3.7k
SecHack365 北海道会 LT
kajyuuen
0
550
Other Decks in Programming
See All in Programming
Vibes Containers 〜AIで変わるコンテナ設計と運用〜
tkikuc
3
540
Claude Codeを組織的に動かして月400PRを実現した話
happy_ryo
0
270
AI Agent時代のリアーキテクチャ戦略と実践
hokaccha
9
4.4k
選挙速報を多くのユーザーへ 届ける Live Activities 設計
hamayokokuririn
0
140
Omarchy Tokyo やると聞いて UMPC 買ってセットアップしてきた
mtsmfm
0
140
The Good Stuff, Not the Slop: Engineering High-Quality Android Apps with Modern AI Tooling
danybony
1
250
What We Talk About When We Talk About XP
m_seki
2
620
XHTMLが残したもの
yosuke_furukawa
PRO
2
800
AWS Step Functions 大規模並列の壁を越える / jaws-sonic-2026-niigata-step-functions
kasacchiful
PRO
1
450
大喜利で理解するLLM as a Judge / Understanding LLM-as-a-Judge through Ogiri
rockname
0
100
Building an Out-of-Order CPU
latte72
1
770
自分的「カンファレンスの楽しみ方」
syumai
0
200
Featured
See All Featured
The innovator’s Mindset - Leading Through an Era of Exponential Change - McGill University 2025
jdejongh
PRO
1
330
A designer walks into a library…
pauljervisheath
211
25k
No one is an island. Learnings from fostering a developers community.
thoeni
21
3.8k
What the history of the web can teach us about the future of AI
inesmontani
PRO
1
690
Documentation Writing (for coders)
carmenintech
77
5.5k
"I'm Feeling Lucky" - Building Great Search Experiences for Today's Users (#IAC19)
danielanewman
230
23k
実際に使うSQLの書き方 徹底解説 / pgcon21j-tutorial
soudai
PRO
202
76k
The Art of Programming - Codeland 2020
erikaheidi
57
14k
Between Models and Reality
mayunak
4
460
Highjacked: Video Game Concept Design
rkendrick25
PRO
1
460
Discover your Explorer Soul
emna__ayadi
2
1.3k
Chasing Engaging Ingredients in Design
codingconduct
0
310
Transcript
ઐ༻ޠநग़ख๏ͷݚڀͱ நग़ΞϓϦέʔγϣϯͷ։ൃ
ࣗݾհ • খྛᕣՏ: @kajyuuen • ஜେֶ ใֶ܈ 4 • ݚڀࣗવݴޠॲཧɺػցֶश
• ։ൃͰRuby on RailsΛΑ͍͘·͢ • झຯ • ΠϯλʔωοτɺԻָؑɺόΠΫ(͓ٳΈத) 2
త 3 ڭࢣσʔλ͕গͳ͍ઐυϝΠϯͷจষ͔Β ઐ༻ޠΛநग़͕ग़དྷΔγεςϜɾख๏ͷ։ൃ
ઐ༻ޠͱ ઐ༻ޠʢͤΜΜΑ͏͝ʣͱɺ͋Δಛఆͷ৬ۀʹैࣄ͢Δऀɺ ͋ΔಛఆͷֶͷɺۀքͷؒͰͷΈ༻͞Εɺ௨༻͢Δݴ༿ɾ༻ޠ܈Ͱ͋Δɻ ςΫχΧϧλʔϜʢӳޠ technical termʣͱݴΘΕΔɻ Wikipedia͔ΒͷҾ༻ 4 ྫ: ίʔϧηϯλʔ
• ΦϖϨʔλʔɺFAQɺVoCɺฏۉ௨࣌ؒ ྫ: ྉཧ • ͍ͪΐ͏Γɺܡണ͖ɺࡾຕ͓Ζ͠
എܠ ͔͠͠… • ҰൠͷυϝΠϯͰֶशͨ͠ϞσϧΛ ઐυϝΠϯʹదԠ্ͤͯ͞ख͘நग़ग़དྷͳ͍ • ઐ༻ޠͷநग़ʹઐՈͷଟ͘ͷ࣌ؒͱਓख͕ඞཁ ͱ͍͏͕͋Γɺઐ༻ޠͷநग़͔ͬͨ͠ 5 ઐ༻ޠͷࣙॻܗଶૉղੳݕࡧͷਫ਼Λ্ͤ͞Δ
എܠ ͦͷͨΊগͳ͍ίετͰઐ༻ޠநग़͕ՄೳʹͳΔ͜ͱ ϨτϦόͷੑೳ্ʹܨ͕Δ 6
ఏҊख๏ • ग़ݱසͱ࿈සʹΑΔઐ༻ޠͷީิநग़ • ೳಈֶशΛ༻͍ͨڭࢣ͋ΓֶशʹΑΔઐ༻ޠީิͷྨ 7 ͜ΕΒ2ͭͷख๏ΛΈ߹ΘͤΔ͜ͱͰ ίετͰͷઐ༻ޠநग़ΛՄೳʹ͢Δ
ઐ༻ޠநग़·Ͱͷϑϩʔ 8 ग़ݱසͱ࿈සʹΑΔઐ༻ޠީิநग़ ೳಈֶशΛ༻͍ͨڭࢣ͋Γֶश ઐ༻ޠͷநग़
ग़ݱසͱ࿈සʹΑΔઐ༻ޠީิநग़[த+ 2003] • ઐ༻ޠ໊ࢺͦͷͷ͔ෳͷ໊ࢺͷෳ߹ޠ͔ΒΔͱԾఆ • ෳ߹ޠΛߏ͢Δ࠷খ୯ҐΛ୯໊ࢺͱఆٛ • ͋Δ୯໊ࢺ͕ଞͷ୯໊ࢺͱ࿈݁ͯ͠ ෳ߹ޠΛ࡞Δճ͕ଟ͍΄Ͳॏཁ㱺ઐ༻ޠ 9
ࣗવݴޠॲཧ ࣗવ ݴޠ ॲཧ = + +
ग़ݱසͱ࿈සʹΑΔઐ༻ޠީิநग़[த+ 2003] ྫ: ࣗવݴޠॲཧ 10 ୯໊ࢺ લͷޠʹ࿈݁ͨ͠ճ ޙͷޠʹ࿈݁ͨ͠ճ ࣗવ
ݴޠ ॲཧ ॏཁ = ෳ߹ޠΛ࡞Δ୯໊ࢺͷ࿈݁ճͷ૬ฏۉ = 6 1 ⋅ 2 ⋅ 2 ⋅ 3 ⋅ 1 ⋅ 1 = 1.51
ઐ༻ޠநग़·Ͱͷϑϩʔ 11 ग़ݱසͱ࿈සʹΑΔઐ༻ޠީิநग़ ೳಈֶशΛ༻͍ͨڭࢣ͋Γֶश ઐ༻ޠͷநग़
ೳಈֶशͱ ࢁͷϥϕϧͳ͠σʔλͷத͔Β ϥϕϧ͕͘ͱϞσϧͷੑೳ্͕ͦ͠͏ͳσʔλΛϢʔβʹਪન͠ Ξϊςʔγϣϯ͍ͯ͘͜͠ͱͰϞσϧΛֶश͍ͯ͘͠ํ๏ 12 গͳ͍ڭࢣσʔλͰϞσϧͷੑೳ্͕͢Δ
ೳಈֶशͱ 13 ઐ༻ޠ ඇઐ༻ޠ ϥϕϧͳ͠ 1 2 ϥϕϧ͕Γ͍ͨσʔλ?
ೳಈֶशͱ 14 ઐ༻ޠ ඇઐ༻ޠ ϥϕϧͳ͠ 1 2 ϥϕϧ͕Γ͍ͨσʔλ? ޮՌతͳֶश͕ߦ͑ͳ͍
ೳಈֶशͱ 15 ઐ༻ޠ ඇઐ༻ޠ ϥϕϧͳ͠ 1 2 ϥϕϧ͕Γ͍ͨσʔλ? ֶश͕ޮՌతʹਐΉ
ಛྔϕΫτϧͷ࡞ • લޙೋ୯ޠͷදܥͱࢺͱจࣈछ • ڭࢣͳֶ͠शʹΑΔॏཁ ͔ΒಛྔϕΫτϧΛ࡞͢Δ 16 ݚڀ ࣗવݴޠॲཧ
ͱ ػց ֶश Ͱ͢ ໊ ॿ ઐ༻ޠީิ ॿ ໊ ໊ ॿಈ ݚڀ ࣗવݴޠॲཧ ͱ ػց ֶश Ͱ͢ 1.51 ݚڀ ࣗવݴޠॲཧ ͱ ػց ֶश Ͱ͢
Ϟσϧͷֶश Logistic regression • ͦͷ୯ޠ͕ઐ༻ޠ͔ඇઐ༻ޠ͔Λྨ͢ΔϞσϧ • ೳಈֶशͰֶशͱ༧ଌΛ܁Γฦ͢ҝ୯७ͳϞσϧΛ࠾༻ • ࠓճ༻͍Δೳಈֶशͷख๏Ͱ༧ଌ͕֬ඞཁ 17
σʔλબͱϞσϧͷߋ৽ Uncertainly Sampling (least confident) ݱ࣌ͷϞσϧͰ࠷ෆ͔֬ͳσʔλΛਪન 18 x* LC =
arg max x∈U 1 − Pθ ( ̂ y|x) ̂ y: ࠷औΓ͏Δ͕֬ߴ͍ϥϕϧ U : ϥϕϧͳ͠σʔλͷू߹ x* LC : ϥϕϧ͚Λਪન͢Δσʔλ
࣮ݧᶃ: Wikipediaʹରͯ͠ઐ༻ޠநग़ • σʔλ • Wikipediaͷจষ61ͭʹରͯ͠ઐ༻ޠͷநग़Λߦ͏ • ݅ઃఆ • ڭࢣͳֶ͠शͰநग़ͨ͠༻ޠͷࡾͷҰʹΞϊςʔγϣϯ
• 5ͭͷσʔλʹϥϕϦϯά͕ऴΘͬͨΒϞσϧΛ࠶ֶश • ೳಈֶशͱϥϯμϜαϯϓϦϯάɺࣙॻʹΑΔൺֱΛߦ͏ 19 ೳಈֶश͕ϥϯμϜαϯϓϦϯάΑΓ༏Ε͍ͯΔ͜ͱΛࣔ͢
࣮ݧᶃ: ݁Ռ IPAdic NEologd 20 Ϟσϧ 1SFDJTJPO 3FDBMM 'WBMVF ڭࢣͳֶ͠श
ϥϯμϜαϯϓϦϯά ೳಈֶश Ϟσϧ 1SFDJTJPO 3FDBMM 'WBMVF ڭࢣͳֶ͠श ϥϯμϜαϯϓϦϯά ೳಈֶश • ྆ࣙॻʹ͓͍ͯϥϯμϜαϯϓϦϯάΑΓೳಈֶश͕༏Ε͍ͯͨ • NEologdΛ༻ͨ͠΄͏͕ੑೳ͕ߴ͔ͬͨ
࣮ݧᶄ: FAQυϝΠϯʹରͯ͠ͷઐ༻ޠநग़ • ֶशσʔλ • εΧύʔʂͷϔϧϓίϯςϯπ͔Βऔಘͨ͠FAQ 5,113จࣈ • ݅ઃఆ •
ϥϯμϜʹΞϊςʔγϣϯ͢ΔϞσϧͱൺֱ • 5ͭͷσʔλʹϥϕϦϯά͕ऴΘͬͨΒϞσϧΛ࠶ֶश • Ξϊςʔγϣϯ͕0ͷͱ͖શͯͷநग़୯ޠΛઐ༻ޠͱΈͳ͢ 21 ͲͷఔΞϊςʔγϣϯ͢Ε࣮༻తͳϞσϧʹͳΔ͔֬ೝ IUUQTIFMQDFOUFSTLZQFSGFDUWDPKQ
࣮ݧᶄ: ਫ਼ͱ࠶ݱ 22 • ਫ਼ೳಈֶश͕ϥϯμϜαϯϓϦϯάΑΓઌʹανΔ • ࠶ݱͰೳಈֶशϥϯμϜαϯϓϦϯάΛେ্͖͘ճΔ Ξϊςʔγϣϯͳͩ͠ͱ ਫ਼͍ ڭࢣͳֶ͠श
ઐ༻ޠͷ72.7%ΛΧόʔ ڭࢣͳֶ͠श
࣮ݧᶄ: F 23 ׂ࢛ఔΞϊςʔγϣϯΛߦ͏͚ͩͰF7ׂΛ͑ͨ ࠷େͰ20ϙΠϯτͷࠩ
நग़ʹޭͨ͠ઐ༻ޠ • εΧύʔʂɺϓϨϛΞϜαʔϏεޫϚϯγϣϯ͚αʔϏε நग़ग़དྷͳ͔ͬͨઐ༻ޠ • TZ-WR4KPɺSP-HR200HɺΞϯςφαϙʔτϓϥϯ ؒҧͬͯநग़ͯ͠͠·ͬͨ୯ޠ • ൪ɺνϟϯωϧɺMyνϟϯωϧ1 ࣮ݧᶄ:
ڭࢣͳֶ͠शͰͷநग़୯ޠྫ 24
ΠϯλʔϑΣʔε ΞϊςʔγϣϯͷޮΛ্͛ΔͨΊʹ WebΞϓϦέʔγϣϯͱͯ͠ΠϯλʔϑΣʔεΛ։ൃͨ͠ 25 ػೳҰཡ • ઐ༻ޠͷϋΠϥΠτ / நग़ػೳ •
ೳಈֶशʹΑΔֶशͱΞϊςʔγϣϯσʔλͷਪન • CSVΤΫεϙʔτ
DEMO 26
ΞϓϦέʔγϣϯͷߏ 27
·ͱΊ 28 త গͳ͍ςΩετσʔλ͔Βઐ༻ޠͷநग़Λߦ͏ ख๏ ڭࢣͳֶ͠श+ೳಈֶशΛ༻͍ͨWebΞϓϦέʔγϣϯͷఏڙ ࠓޙ நग़ΞϧΰϦζϜͷ࠶࣮ʹΑΔߴԽ ݕࡧͳͲͷԠ༻ʹ͓͚ΔੑೳධՁɺ৽ͨͳख๏ɾಛྔͷௐࠪ
ࢀߟจݙ [1] த ༟ࢤ, ౬ຊ ߛজ, ୢଇ. ग़ݱසͱ࿈සʹجͮ͘ઐ༻ޠநग़. ࣗવݴޠॲཧ.
2003, 10(1), p.27-45. [2] த ༟ࢤ, ౬ຊ ߛজ, ୢଇ. ຊޠϚχϡΞϧจʹ͓͚Δ໊ࢺؒͷ࿈ใΛ༻͍ͨϋΠύʔςΩε τԽͷͨΊͷࡧҾޠͷநग़. ใॲཧֶձݚڀใࠂࣗવݴޠॲཧ. 1996, (114), p.65-72 [3] “ઐ༻ޠʢΩʔϫʔυʣࣗಈநग़༻PerlϞδϡʔϧ ”. ”ઐ༻ޠʢΩʔϫʔυʣࣗಈநग़γεςϜ”ͷ ϖʔδΑ͏ͦ͜. http://gensen.dl.itc.u-tokyo.ac.jp/termextract.html, (ࢀর 2018-9-4). [4] Burr Settles. Active Learning Literature Survey. Computer Sciences Technical Report 1648. 2010. http://burrsettles.com/pub/settles.activelearning.pdf, (ࢀর 2018-9-4). [5] Burr Settles, Mark Craven. An Analysis of Active Learning Strategies for Sequence Labeling Tasks. EMNLP. 2008. 29