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
言語処理100本ノックをRubyでやったメモ
Search
himkt
August 06, 2016
2.7k
11
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
言語処理100本ノックをRubyでやったメモ
himkt
August 06, 2016
More Decks by himkt
See All by himkt
Linformer: paper reading
himkt
0
890
RoBERTa: paper reading
himkt
1
460
NLP SoTA 勉強会 / ner_2019
himkt
2
1.5k
自然言語処理 @ クックパッド / nlp at cookpad
himkt
1
600
Interpretable Machine Learning 6.3 - Prototypes and Criticisms
himkt
2
240
ニューラル固有表現抽出 / Neural Named Entity Recognition
himkt
3
870
ニューラル固有表現抽出器を実装してみる / PyNER
himkt
6
2.3k
Spacyでお手軽NLP / NLP with spacy
himkt
0
1.1k
Deep Learning Book 10その2 / deep learning book 10 vol2
himkt
2
220
Featured
See All Featured
Why Our Code Smells
bkeepers
PRO
340
58k
The State of eCommerce SEO: How to Win in Today's Products SERPs - #SEOweek
aleyda
2
12k
How to Align SEO within the Product Triangle To Get Buy-In & Support - #RIMC
aleyda
2
1.9k
The Mindset for Success: Future Career Progression
greggifford
PRO
0
550
How STYLIGHT went responsive
nonsquared
100
6.3k
Navigating the moral maze — ethical principles for Al-driven product design
skipperchong
2
670
Practical Orchestrator
shlominoach
192
12k
Embracing the Ebb and Flow
colly
88
5.2k
Design and Strategy: How to Deal with People Who Don’t "Get" Design
morganepeng
133
20k
How People are Using Generative and Agentic AI to Supercharge Their Products, Projects, Services and Value Streams Today
helenjbeal
2
380
The Myth of the Modular Monolith - Day 2 Keynote - Rails World 2024
eileencodes
28
3.7k
From Legacy to Launchpad: Building Startup-Ready Communities
dugsong
0
370
Transcript
ݴޠॲཧ100ຊϊοΫΛRubyͰΔ ʢsciruby-jp issue #2ʣ
ࣗݾհͱͬͨ͜ͱ • B4 at ஜେֶ ʢࣗવݴޠॲཧ? ػցֶश? ʣ • ݚڀɿใநग़ʢ֬Ϟσϧʣ
• ୲ɿݴޠॲཧ100ຊϊοΫΛRubyͰղ͍ͯΈΔ • ύοέʔδϢʔβ https://github.com/himkt/nlp-100knock
ݴޠॲཧ100ຊϊοΫ • ౦େֶ סɾԬ࡚ݚ͕ެ։͍ͯ͠ΔࣗવݴޠॲཧυϦϧ • ఆ͞ΕΔݴޠPython • ୈ8ষʙୈ10ষ͕Պֶܭࢉతʁʢػցֶशʣͳ ʢը૾: http://www.cl.ecei.tohoku.ac.jp/nlp100/ʣ
RubyͰݴޠॲཧ100ຊϊοΫ • GitHubͳͲͰݕࡧ͢Δͱ… • RubyͰΖ͏ͱ͍ͯ͠Δਓ͍Δ • ͕ɼ4ষ͘Β͍·ͰͰߋ৽్͕ઈ͍͑ͯΔ ɹ • ఆݴޠɿPython
• RubyͰͰ͖ΔʁʢͰ͖ΔͩΖ͏ʣ -> ࣮ࡍʹղ͍ͯΈΔ ɹͰ͖ͳ͍͜ͱ͕ز͔ͭ͋Δ͜ͱ͕Θ͔ͬͨ
ओͳτϐοΫ • 72ɿૉੑநग़ • 73ɿϩδεςΟοΫճؼ • 78ɿΫϩεόϦσʔγϣϯ • 85ɿओੳ •
90ɿword2vec • 97ɿk-means • 98ɿWard๏ɿͰ͖ͳ͔ͬͨ… • 99ɿt-SNE
ओͳτϐοΫ • 72ɿૉੑநग़ • 73ɿϩδεςΟοΫճؼ • 78ɿΫϩεόϦσʔγϣϯ • 85ɿओੳ •
90ɿword2vec • 97ɿk-means • 98ɿWard๏ • 99ɿt-SNE 6
ૉੑநग़ • ࣗવݴޠॲཧʹ͓͍ͯૉੑʹͳΔͷɿ୯ޠʢଟ͘ͷ߹ʣ • ग़ݱ͢Δ୯ޠͷͱͯଟ͍ʢສ - ेສʣ • ͯ͢ͷ୯ޠΛૉੑͱͯ͠͏ͱֶश͕͏·͍͔͘ͳ͍ •
ޮతͳૉੑநग़͕ඞཁ • Python:scikit-learn::feature_extraction • Ruby:ܾఆ൛తͳϥΠϒϥϦଘࡏ͠ͳ͍ • ࠓճ͓खʢhttps://github.com/himkt/rblearnʣ
ओͳτϐοΫ • 72ɿૉੑநग़ • 73ɿϩδεςΟοΫճؼ • 78ɿΫϩεόϦσʔγϣϯ • 85ɿओੳ •
90ɿword2vec • 97ɿk-means • 98ɿWard๏ • 99ɿt-SNE 8
ϩδεςΟοΫճؼ • ϥΠϒϥϦ • Statsample-glmɿDaruͱҰॹʹ͏͜ͱ͕ఆ͞Ε͍ͯΔʁ • Liblinear-RubyɿNMatrix, NArrayʹରԠ͍ͯ͠ͳ͍ • σʔλϑϨʔϜɿΧϥϜ͕ଟ͍σʔλΛѻ͏ͷʹ͔ͳ͍ʁ*
• ࢥ͍ࠐΈ͔Εͳ͍ʢࠓճͷσʔλ10000 * 10000͘Β͍ʣ • NArrayͰ࣮ͨ͠ • ඞཁͳͷɿίετؔͱޯ • ߦྻͷੵͰදݱՄೳʢNArrayͷػೳ͚ͩͰ࣮Մʣ
ओͳτϐοΫ • 72ɿૉੑநग़ • 73ɿϩδεςΟοΫճؼ • 78ɿΫϩεόϦσʔγϣϯ • 85ɿओੳ •
90ɿword2vec • 97ɿk-means • 98ɿWard๏ • 99ɿt-SNE
ΫϩεόϦσʔγϣϯ • σʔληοτΛׂͯ͠ෳճֶशΛߦ͏ ͜ͱͰ༧ଌϞσϧͷ൚ԽੑೳΛௐΔ • Python: sklearn::cross_validation • ྻͷΠϯσοΫεΛฦ͍ͯ͠Δ͚ͩ •
Integer array indexing (masking ?) • NArrayʹ͋Δ NMatrixʹͳ͍ ը૾ɿhttps://pydata.tokyo/ipynb/tutorial-1/ml.html ࢀߟɿhttp://watanabe-www.math.dis.titech.ac.jp/users/swatanab/cross-val.html
ΫϩεόϦσʔγϣϯ • Ruby: ݱঢ়ͰϥΠϒϥϦଆͰ࣮͞Ε͍ͯͨΓ͢Δ • e.g. Liblinear.cross_validation (liblinear-ruby) • Python:
scikit-learn::cross_validation • ϞσϧʢLogistic Regressionʣ܇࿅σʔλΛड͚औΓֶश͢Δ͚ͩ ΫϩεόϦσʔγϣϯ͢ΔϥΠϒϥϦΛ࡞ͬͨʢhttps://github.com/himkt/rblearnʣ ΫϩεόϦσʔγϣϯͱ ֶशͷϩδοΫ͕
ओͳτϐοΫ • 72ɿૉੑநग़ • 73ɿϩδεςΟοΫճؼ • 78ɿΫϩεόϦσʔγϣϯ • 85ɿओੳ •
90ɿword2vec • 97ɿk-means • 98ɿWard๏ • 99ɿt-SNE
ओੳ
ओੳ
ओੳ • ϥΠϒϥϦ • Ruby: statsample • σʔλ͕Ͱ͔͍ͷͰɼૄߦྻͷ··ѻ͏ඞཁ͕͋Δ • DataFrameΛͭ͘Δඞཁ͕͋Δʁ
• ݻ༗ɾݻ༗ϕΫτϧܭࢉͱͯ͠ղ͘ • NArray, NMatrixʢs.t. ૄߦྻʣ • NArray: ૄߦྻ·ͩରԠ͍ͯ͠ͳ͍ • NMatrix: ૄߦྻͷݻ༗ɾݻ༗ϕΫτϧܭࢉະ࣮ -> อཹ
ओͳτϐοΫ • 72ɿૉੑநग़ • 73ɿϩδεςΟοΫճؼ • 78ɿΫϩεόϦσʔγϣϯ • 85ɿओੳ •
90ɿword2vec • 97ɿk-means • 98ɿWard๏ • 99ɿt-SNE
word2vec • ϥΠϒϥϦ • Python: gensim • Ruby: ແ͍ʢଟʣ •
NArrayͰ࣮ • word2vecϞσϧΛ܇࿅ͨ͠ޙʹ୯ޠϕΫτϧ͕ಘΒΕΕྑ͍ • ࣮ࡍʹඞཁͳͷϕΫτϧಉ࢜ͷίαΠϯྨࣅͷܭࢉ͚ͩ ʢNArray NMatrixͷػೳͰॆʣ • NArrayͷ΄͏͕͔ͬͨͷͰNArrayΛͬͨ
ओͳτϐοΫ • 72ɿૉੑநग़ • 73ɿϩδεςΟοΫճؼ • 78ɿΫϩεόϦσʔγϣϯ • 85ɿओੳ •
90ɿword2vec • 97ɿk-means • 98ɿWard๏ • 99ɿt-SNE
k-means t-SNE • ϥΠϒϥϦ • Python: sklearn.clustering • Ruby: AI4Rʢhttp://ai4r.org/ʣ
• NArray NMatrixະରԠ • ߋ৽ࢭ·ͬͯΔʁ • NArray͚ͩͰ࣮ͨ͠ʢNArrayͷ΄͏͕͍ʣ • ಛʹ٧·Δ͜ͱͳ࣮͘Ͱ͖Δ
·ͱΊ • ݴޠॲཧ100ຊϊοΫΛղ͍ͯΈͨ • ͍͍ͩͨNArray, NMatrix͕͋Εղ͚Δ • େنͳσʔλͷओੳͱ͔Ͱ͖ͳ͍ • scikit-learnΈ͍ͨͳϥΠϒϥϦ͕ඞཁ͔ʁ
• աڈϩάΛݟͨʢࡢʣ • ༗Εخ͍͠ʢRubyࣗવݴޠॲཧʹ͍͍ͯΔͱࢥ͏ʣ • ϥΠϒϥϦ: NArrayͳΓNMatrixͳΓDaruͷVector?ͳΓ ͳΜΒ͔ͷܾΊΒΕͨσʔλߏ͕౷Ұతʹ͑ͯ΄͍͠ • ΫϩεόϦσʔγϣϯͱ͔ૉੑநग़ͱ͔
΄͍͠ • NArray: ૄߦྻରԠ • NMatrix: linalgͷૄߦྻରԠ • NArray, NMatrix:
ΦϒδΣΫτͷγϦΞϥΠζ • NMatrix: Integer Array indexing • Feature Extractor, Feature Vectorizer