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
CBoW入門
Search
Kento Nozawa
April 21, 2016
Research
4
3.6k
CBoW入門
2016年4月22日の機械学習勉強会の資料
Continuous Bag of Wordsの入門スライドです
Kento Nozawa
April 21, 2016
Tweet
Share
More Decks by Kento Nozawa
See All by Kento Nozawa
Analysis on Negative Sample Size in Contrastive Unsupervised Representation Learning
nzw0301
0
130
[IJCAI-ECAI 2022] Evaluation Methods for Representation Learning: A Survey
nzw0301
0
570
[NeurIPS Japan meetup 2021 talk] Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
nzw0301
0
160
[IBIS2021] 対照的自己教師付き表現学習おける負例数の解析
nzw0301
0
150
Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning
nzw0301
0
460
Introduction of PAC-Bayes and its Application for Contrastive Unsupervised Representation Learning
nzw0301
2
770
NLP Tutorial; word representation learning
nzw0301
0
180
Analyzing Centralities of Embedded Nodes
nzw0301
0
140
Paper Reading: Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics
nzw0301
2
1.1k
Other Decks in Research
See All in Research
Weekly AI Agents News! 10月号 論文のアーカイブ
masatoto
1
500
ナレッジプロデューサーとしてのミドルマネージャー支援 - MIMIGURI「知識創造室」の事例の考察 -
chiemitaki
0
210
医療支援AI開発における臨床と情報学の連携を円滑に進めるために
moda0
0
150
メタヒューリスティクスに基づく汎用線形整数計画ソルバーの開発
snowberryfield
3
760
Global Evidence Summit (GES) 参加報告
daimoriwaki
0
240
Large Vision Language Model (LVLM) に関する最新知見まとめ (Part 1)
onely7
24
5.9k
国際会議ACL2024参加報告
chemical_tree
1
430
ラムダ計算の拡張に基づく 音楽プログラミング言語mimium とそのVMの実装
tomoyanonymous
0
400
Evaluating Tool-Augmented Agents in Remote Sensing Platforms
satai
2
150
Data-centric AI勉強会 「ロボットにおけるData-centric AI」
haraduka
0
440
新規のC言語処理系を実装することによる 組込みシステム研究にもたらす価値 についての考察
zacky1972
1
320
Weekly AI Agents News! 12月号 プロダクト/ニュースのアーカイブ
masatoto
0
320
Featured
See All Featured
ピンチをチャンスに:未来をつくるプロダクトロードマップ #pmconf2020
aki_iinuma
114
50k
GraphQLとの向き合い方2022年版
quramy
44
13k
A designer walks into a library…
pauljervisheath
205
24k
Facilitating Awesome Meetings
lara
52
6.2k
It's Worth the Effort
3n
184
28k
For a Future-Friendly Web
brad_frost
176
9.5k
Designing for humans not robots
tammielis
250
25k
Visualizing Your Data: Incorporating Mongo into Loggly Infrastructure
mongodb
45
9.4k
Building Better People: How to give real-time feedback that sticks.
wjessup
367
19k
Build your cross-platform service in a week with App Engine
jlugia
229
18k
Speed Design
sergeychernyshev
27
790
Building Applications with DynamoDB
mza
93
6.2k
Transcript
Continuous Bag of Wordsೖ @ػցֶशษڧձ 201604݄22ʢۚʣ M1
ࠓ͢͜ͱ • ଟύʔηϓτϩϯ (MLP) • Continuous Bag of Words •
word2vecʹ͋ΔยํͷϞσϧ • ߴԽNGʹ͍ͭͯݴٴ͠·ͤΜ
ଟύʔηϓτϩϯͷ͓͞Β͍ • ؙɿ1ͭͷΛड͚ͯɼؔΛద༻ͯ͠1ͭͷΛग़ྗ ʢؙ1ͭΛϢχοτɼؔΛ׆ੑԽؔʣ • ҹɿϢχοτͷग़ྗͱॏΈʢʣͷੵΛ࣍ͷʹ Ͱ͖Δ͚ͩਖ਼ղ͢ΔΑ͏ͳॏΈΛٻΊΔ Input layer hidden
layer output layer (soft max) x1 h3 h1 h2 x2 x3 x4 0.2 0.5 0.3
ଟύʔηϓτϩϯͷ۩ମྫ • 4୯ޠ͔͠ͳ͍ੈքΛߟ͑Δ • [jobs, mac, win8, ms] • ೖྗɿจॻ
• ग़ྗɿ֬ʢೖྗจॻ͕”mac”͔”windowns”ʣ Input layer hidden layer output layer (softmax) jobs h3 h1 h2 mac win8 ms p(mac)=0.2 p(win)=0.8
۩ମྫɿೖྗ ͦΕͧΕ୯ޠͷස͕ೖྗͷೖྗ • doc0: [win8, win8, ms, ms, ms, jobs]
-> ms • doc1: [jobs, mac, mac, mac, mac, mac, mac] -> mac Input layer hidden layer output layer (softmax) jobs=1 h3 h1 h2 mac=0 win8=2 ms=3 Input layer hidden layer output layer (softmax) jobs=1 h3 h1 h2 mac=6 win8=0 ms=0 doc0 doc1
۩ମྫɿӅΕ ೖྗ-ӅΕؒͷॏΈߦྻWɼ3x4ͷߦྻ ӅΕɼ(ೖྗͷग़ྗ)x(ॏΈ)ͷhΛड͚औΔ doc0 2 4 1 2 3 0
1 2 1 2 1 1 1 1 3 5 2 6 6 4 1 0 2 3 3 7 7 5 = 2 4 7 9 5 3 5 Input layer hidden layer output layer (softmax) jobs=1 f(5)=0.99 f(7)=0.99 f(9)=0.99 mac=0 win8=2 ms=3 Wx = h
۩ମྫɿӅΕ ೖྗ-ӅΕؒͷॏΈߦྻWɼ3x4ͷߦྻ ӅΕɼ(ೖྗͷग़ྗ)x(ॏΈ)ͷhΛड͚औΔ doc0 2 4 1 2 3 0
1 2 1 2 1 1 1 1 3 5 2 6 6 4 1 0 2 3 3 7 7 5 = 2 4 7 9 5 3 5 Input layer hidden layer output layer (softmax) jobs=1 f(5)=0.99 f(7)=0.99 f(9)=0.99 mac=0 win8=2 ms=3
۩ମྫɿӅΕ ׆ੑԽؔ f(x) Λ௨ͯ͠ӅΕ͔Βग़ྗ doc0 Input layer hidden layer output
layer (softmax) jobs=1 f(5)=0.99 f(7)=0.99 f(9)=0.99 mac=0 win8=2 ms=3 By Chrislb - created by Chrislb, CC දࣔ-ܧঝ 3.0, https://commons.wikimedia.org/w/index.php?curid=223990 ؔྫɿγάϞΠυؔ
۩ମྫɿग़ྗ ӅΕ-ग़ྗͷॏΈW’ɼ2x3ͷߦྻ ग़ྗɼ(ӅΕͷग़ྗ)x(ॏΈ)ͷΛड͚औΔ doc0 Input layer hidden layer output layer
(softmax) jobs=1 f(5)=0.99 f(7)=0.99 f(9)=0.99 mac=0 win8=2 ms=3 -0.1 0.1 1 1 1.01 1 1 1.01 2 4 0.99 0.99 0.99 3 5 = 1.0 1.0 W0f(h) = u o
ग़ྗͷ׆ੑԽؔ ग़ྗͷ׆ੑԽؔɿ֬Λग़ྗ͢Δsoftmaxؔ doc0(=[win8, win8, ms, ms, ms, jobs])0.54Ͱwinͷจॻ Input layer
hidden layer output layer (softmax) jobs=1 f(5)=0.99 f(7)=0.99 f(9)=0.99 mac=0 win8=2 ms=3 -0.1 0.1 p(mac)=0.46 p(win)=0.54 exi P n exn e0.1 e0.1 + e 0.1 = 0.54 e 0.1 e0.1 + e 0.1 = 0.46
ֶश • ޡࠩٯ๏ΛͬͯॏΈW, W’ Λௐઅ͠ɼdoc0͕win ʹͳΔ֬ΛߴΊΔΑ͏ʹֶश • doc0ͱ͖ɼޡࠩͷݩʹͳΔͷਖ਼ղϥϕϧ [0, 1]
Input layer hidden layer output layer (softmax) jobs=1 f(5)=0.99 f(7)=0.99 f(9)=0.99 mac=0 win8=2 ms=3 -0.1 0.1 p(mac)=0.46 p(win)=0.54
CBoWͷΞϧΰϦζϜ MLP͕Θ͔Εָͳͣɽɽɽɽ
one—hotදݱ • ୯ޠΛޠኮ࣍ݩVͷϕΫτϧͰදݱ • ରԠ͢Δ࣍ݩ͚ͩ1ɼΓ0 ྫɿ͠{I, drink, coffee, everyday} ͳΒ
I = [1, 0, 0, 0] drink = [0, 1, 0, 0] coffee = [0, 0, 1, 0] everyday = [0, 0, 0, 1]
จ຺૭෯ ͋Δจʹ͓͍ͯ͢Δ1୯ޠͷपғn୯ޠΛѻ͏ ͜ͷͱ͖ɼnΛจ຺૭෯ͱ͍͏ Q. I drink coffee everydayͰจ຺૭෯2ҎԼʹग़ݱ͢Δ Bog of
Wordsʁ A. [I, drink, everyday]
Continuous Bag of Wordsɿ֓ཁ • 3ͷχϡʔϥϧωοτ • ೖྗɿจ຺૭෯ҎԼͰڞى͢Δ୯ޠ • ग़ྗɿ1୯ޠͷ֬
Continuous Bag of Wordsɿೖྗ MLPͷೖྗ͕ਤͷೖྗͷശ1ͭʹ૬ Input layer hidden layer output
layer (softmax) jobs=1 f(5)=0.99 f(7)=0.99 f(9)=0.99 mac=0 win8=2 ms=3 MLP
Continuous Bag of Wordsɿೖྗ • ശ1ͭone-hotදݱΛड͚औΔ • I drink coffee
everyday Ͱw(t)=coffee drink= [0, 1, 0, 0] ͕͍෦ͷͱΔ coffee
Continuous Bag of Wordsɿೖྗ I = [0, 1, 0, 0]
drink= [0, 1, 0, 0] everyday = [0, 0, 0, 1] coffee
Continuous Bag of Wordsɿೖྗ-ӅΕͷॏΈ • ҹ1ͭʹରͯ͠ɼॏΈߦྻ • ͜ͷॏΈߦྻڞ༗ WN⇥V 2
4 1 2 3 0 1 2 1 2 1 1 1 1 3 5 2 6 6 4 0 1 0 0 3 7 7 5 = 2 4 2 2 1 3 5 Wx = ut 1
Continuous Bag of Wordsɿೖྗ-ӅΕͷॏΈ • ҹ1ͭʹରͯ͠ɼॏΈߦྻ • ͜ͷॏΈߦྻڞ༗ • ೖྗone–hotΑΓɼ୯ޠϕΫτϧ͕ӅΕʹ
WN⇥V 2 4 1 2 3 0 1 2 1 2 1 1 1 1 3 5 2 6 6 4 0 1 0 0 3 7 7 5 = 2 4 2 2 1 3 5 Wx = ut 1
Continuous Bag of WordsɿӅΕ • ୯ޠϕΫτϧͷฏۉ͕ӅΕͷೖྗʢN࣍ݩϕΫτϧʣ • ׆ੑԽؔͳ͠ ut 2
+ ut 1 + ut+1 3 = h 1 3 0 @ 2 4 1 1 1 3 5 + 2 4 2 2 1 3 5 + 2 4 0 2 1 3 5 1 A = 2 4 1 1.67 0.33 3 5
Continuous Bag of WordsɿӅΕ-ग़ྗ ॏΈߦྻ ͱӅΕͷग़ྗʢฏۉϕΫτϧʣͷੵ W0V ⇥N 2 6
6 4 1 2 1 1 2 1 1 2 2 0 2 0 3 7 7 5 2 4 1.00 1.67 0.33 3 5 = 2 6 6 4 4.01 2.01 5.00 3.34 3 7 7 5 W0h = u o
Continuous Bag of Wordsɿग़ྗ 1୯ޠͷ༧ଌΛ͍ͨ͠ • ग़ྗͷϢχοτ = ޠኮ =
V • ׆ੑԽؔɿsoftmaxؔ softmax (u o ) = y softmax 0 B B @ 2 6 6 4 4 . 01 2 . 01 5 . 00 3 . 34 3 7 7 5 1 C C A = 2 6 6 4 0 . 23 0 . 03 0 . 62 0 . 12 3 7 7 5
Continuous Bag of Wordsɿग़ྗ I, drink, everydayΛೖΕͯಘΒΕͨ୯ޠͷ֬ 2 6 6
4 0.23 0.03 0.62 0.12 3 7 7 5 coffeeͷ֬
ֶश݁Ռͷ୯ޠϕΫτϧ • ೖྗͱӅΕؒͷॏΈߦྻ͕୯ޠϕΫτϧͷू߹ • 1୯ޠɿ100࣍ݩͱ͔200࣍ݩͰີͳϕΫτϧ
୯ޠϕΫτϧͷخ͍͠ಛੑ • analogy • king-man+woman=queen • Japan-Tokyo+Paris=France • eats-eat+run=runs •
୯ޠͷಛྔ • ਂֶशͷॳظ • ྨࣅܭࢉ • nzwͷ࠷ॳͷจ͜Ε
ࢀߟจݙͳͲ • gensim : https://radimrehurek.com/gensim/ • pythonɼ͕͍ؔΖ͍Ζ͋ͬͯศར • chainer :
https://github.com/pfnet/chainer/tree/master/examples/word2vec • PythonɼχϡʔϥϧωοτͰͷ࣮ྫ • word2vec : https://code.google.com/archive/p/word2vec/ • CɼΦϦδφϧ • word2vec Parameter Learning Explained : http://arxiv.org/pdf/1411.2738v3.pdf • ӳޠɼΘ͔Γ͍͢ղઆ • Efficient Estimation of Word Representations in Vector Spaceɿhttp://arxiv.org/pdf/ 1301.3781.pdf • ӳޠɼCBoWͷͱจɽεϥΠυͷਤͷCBoWͪ͜Β͔Β • ਂֶश Deep Learning. ਓೳֶձ. • ຊޠɼॻ੶