Upgrade to Pro — share decks privately, control downloads, hide ads and more …

word2vecで女性向けQ＆Aサイトを解析してみた

Sponsored · Your Podcast. Everywhere. Effortlessly. Share. Educate. Inspire. Entertain. You do you. We'll handle the rest. →
Avatar for tatsushim tatsushim
June 17, 2015
Research
6.4k
0

 word2vecで女性向けQ＆Aサイトを解析してみた

2015/06/10
IVS CTO NightのLTで発表したプレゼン資料です。
word2vecにmamariQ内のテキストを使用してみました。

Avatar for tatsushim

tatsushim

June 17, 2015

More Decks by tatsushim

See All by tatsushim
DockerとAmazon SageMakerで実現した機械学習システムのプロダクション移行
Avatar for tatsushim tatsushim
0
27k
コミュニティサービスにおける機械学習のためのアノテーション
Avatar for tatsushim tatsushim
0
2.2k
日本のママをコンテナで支える
Avatar for tatsushim tatsushim
1
3.2k
コネヒトが考える技術選択の仕方について
Avatar for tatsushim tatsushim
0
22k
コネヒトが考えるサービスづくりに必要な技術とその考え方について
Avatar for tatsushim tatsushim
2
3.3k
独身男性のためのデータドリブン講座
Avatar for tatsushim tatsushim
0
6.9k

Other Decks in Research

See All in Research
都市交通マスタープランとその後への期待＠熊本商工会議所・熊本経済同友会
Avatar for Traffic Brain trafficbrain
0
210
ScoreMatchingRiesz for Automatic Debiased Machine Learning and Policy Path Estimation with an Application to Japanese Monetary Policy Evaluation
Avatar for MasaKat0 masakat0
0
280
2026年3月1日（日）福島「除染土」の公共利用をかんがえる
Avatar for Masano Atsuko atsukomasano2026
0
610
SoftMatcha 2: 1兆語規模コーパスの超高速かつ柔らかい検索
Avatar for Masataka YONEDA e869120_sub
6
3.4k
LiDAR点群の地表面分類手法の比較・検証
Avatar for vegapunk-hiroshi vegapunkhiroshi79
0
100
重要だけど測れていないもの：高齢者ケアの見えない課題
Avatar for テオリア・テクノロジーズ theoriatec2024
0
300
SAKURAONE: An Open Ethernet-based AI HPC System And Its Observed Workload Dynamics in a Single-Tenant LLM Development Environment
Avatar for Yuuki Tsubouchi (yuuk1) yuukit
1
250
Unified Audio Source Separation (Defense Slides)
Avatar for Kohei Saijo kohei_1979
1
600
AIを叩き台として、 「検証」から「共創」へと進化するリサーチ
Avatar for めーら/Miki mela_dayo
0
270
SOTAのさらに先へ：厳しい推論制約下での高性能モデルのPost-Training
Avatar for Hiroshi Y (RabotniKuma) analokmaus
0
1.1k
ペットのかわいい瞬間を撮影する オートシャッターAIアプリへの スマートラベリングの適用
Avatar for Convergence Lab. mssmkmr
0
500
LLM の Attention 機構まとめ — 数式・計算量・メモリ
Avatar for プワー puwaer
7
1.9k

Featured

See All Featured
Avoiding the “Bad Training, Faster” Trap in the Age of AI
Avatar for Mike Taylor tmiket
0
160
First, design no harm
Avatar for Per Axbom axbom
PRO
2
1.2k
How to optimise 3,500 product descriptions for ecommerce in one day using ChatGPT
Avatar for Katarina Dahlin katarinadahlin
PRO
1
3.6k
Six Lessons from altMBA
Avatar for Skipper Chong Warson skipperchong
29
4.3k
The Success of Rails: Ensuring Growth for the Next 100 Years
Avatar for Eileen M. Uchitelle eileencodes
47
8.1k
Utilizing Notion as your number one productivity tool
Avatar for Mfonobong Umondia mfonobong
4
310
Docker and Python
Avatar for Tania Allard trallard
47
3.9k
Data-driven link building: lessons from a $708K investment (BrightonSEO talk)
Avatar for Szymon Słowik szymonslowik
1
1.1k
Abbi's Birthday
Avatar for Violet Bock coloredviolet
2
7.8k
The Spectacular Lies of Maps
Avatar for Per Axbom axbom
PRO
1
770
How to Ace a Technical Interview
Avatar for Jacob Kaplan-Moss jacobian
281
24k
Designing for humans not robots
Avatar for Tammie Lister tammielis
254
26k

Transcript

  1. word2vecͰঁੑ޲͚QˍA αΠτΛղੳͯ͠Έͨ Tatsuro Shimada <[email protected]> tatsushim @ Connehito, Inc

  2. Connehito Inc. ౡాୡ࿕ʢ͠·ͩͨͭΖ͏ʣ • Connehito, Inc CTO • Πϯϑϥ͔Βϑϩϯτ·Ͱ •

    ͋ͱ͓՛ࢠͷങ͍ग़͠ PROFILE @tatsushim 2

  3. ϚϚϦͱ͸ʁ Connehito Inc. 3

  4. Connehito Inc. ϚϚϦjp (❨web)❩ ϚϚϦ2 (❨ΞϓϦ)❩ ϚϚϦKQ / ϚϚϦ2 ϝσΟΞ

    ίϛϡχςΟ 4

  5. Connehito Inc. ࣭໰΁ͷճ౴ ˋ௒ ճ౴͕ͭ͘·Ͱ ෼ҎԼ ΞϓϦͷ଺ࡏ࣌ؒ ෼Ҏ্ ѹ౗తͳαʔϏεͷ੒௕ 

        +VO +VM "VH 4FQ 0DU /PW %FD +BO 'FC લ݄ൺˋ੒௕ ྦྷܭ౤ߘ਺ ೛৷ɾग़࢈Ͱ೰Ήਓͷ ਓʹਓ͕݄̍ΞΫηε 5

  6. Connehito Inc. ϚϚϦKQ / ϚϚϦ2 6 λʔήοτ ࢲ

  7. Connehito Inc. ϚϚϦKQ / ϚϚϦ2 7 ঁੑ λʔήοτ ࢲ

  8. Connehito Inc. ϚϚϦKQ / ϚϚϦ2 8 ঁੑ ϚϚ λʔήοτ ࢲ

  9. Connehito Inc. ϚϚϦKQ / ϚϚϦ2 9 ঁੑ ϚϚ λʔήοτ ࢲ

    ೛৷ ग़࢈

  10. Connehito Inc. ϚϚϦKQ / ϚϚϦ2 10 ঁੑ ϚϚ λʔήοτ ࢲ

    உੑ ೛৷ ग़࢈

  11. Connehito Inc. ϚϚϦKQ / ϚϚϦ2 11 ঁੑ ϚϚ λʔήοτ ࢲ

    உੑ ೛৷ ग़࢈ ಠΓ਎

  12. Connehito Inc. ϚϚϦKQ / ϚϚϦ2 12 ঁੑ ϚϚ λʔήοτ ࢲ

    உੑ ಠΓ਎ ೛৷ ग़࢈ Ͱ͖ͳ͍

  13. ʘ(^o^)ʗ

  14. ʘ(^o^)ʗ Ϣʔβʔͷ͜ͱཧղ͍ͨ͠ʂ

  15. 15

  16. 16 word2vec

  17. None

  18. Connehito Inc. • Tomas Mikolovࢯ
 (౰࣌Google, ݱFacebook)͕ఏҊ • ୯ޠಉ࢜ͷؔ܎ੑΛϕΫτϧͱͯ͠දݱ •

    ୯ޠͷྨࣅ౓ͷܭࢉ΍଍͠Ҿ͖͕Մೳ word2vecͱ͸ʁ 18

  19. 19

  20. 20 ۩ମྫΛݟͯΈΑ͏

  21. word2vecͷ୅දతͳ2ͭͷ࢖͍ํ Connehito Inc. 21

  22. Display similar words Connehito Inc. 22

  23. None

  24. ྨٛޠ

  25. Interesting properties of the word vectors Connehito Inc. 25

  26. Connehito Inc. 26 word2vecͷྫ vector(‘France')

  27. Connehito Inc. 27 word2vecͷྫ - vector(‘Paris') vector(‘France')

  28. Connehito Inc. 28 word2vecͷྫ - vector(‘Paris') + vector(‘Tokyo’) vector(‘France')

  29. Connehito Inc. 29 word2vecͷྫ - vector(‘Paris') + vector(‘Tokyo’) = vector('Japan')

    vector(‘France')

  30. Connehito Inc. 30 word2vecͷྫ vector(‘Paris')

  31. Connehito Inc. 31 word2vecͷྫ - vector(‘France') vector(‘Paris')

  32. Connehito Inc. 32 word2vecͷྫ - vector(‘France') + vector(‘Italy’) vector(‘Paris')

  33. Connehito Inc. 33 word2vecͷྫ - vector(‘France') + vector(‘Italy’) = vector('Rome')

    vector(‘Paris')

  34. Connehito Inc. 34 word2vecͷྫ vector(‘king')

  35. Connehito Inc. 35 word2vecͷྫ - vector(‘man') vector(‘king')

  36. Connehito Inc. 36 word2vecͷྫ - vector(‘man') + vector(‘woman’) vector(‘king')

  37. Connehito Inc. 37 word2vecͷྫ - vector(‘man') + vector(‘woman’) = vector('queen')

    vector(‘king')

  38. ੔ཧͯ͠ΈΔ Connehito Inc. 38

  39. Connehito Inc. 39 word2vecͷྫ - vector(‘Paris') vector(‘France')

  40. Connehito Inc. 40 word2vecͷྫ - vector(‘Paris') + vector(‘Tokyo’) vector(‘France')

  41. Connehito Inc. 41 word2vecͷྫ - vector(‘Paris') + vector(‘Tokyo’) = vector('Japan')

    vector(‘France')

  42. Connehito Inc. 42 word2vecͷྫ - vector(‘Paris') + vector(‘Tokyo’) = vector('Japan')

    vector(‘France')}

  43. Connehito Inc. 43 word2vecͷྫ - vector(‘Paris') + vector(‘Tokyo’) = vector('Japan')

    vector(‘France') ͋Δ౎ࢢΛट౎ͱ͢Δࠃ }

  44. Connehito Inc. 44 word2vecͷྫ - vector(‘Paris') + vector(‘Tokyo’) = vector('Japan')

    vector(‘France') ͋Δ౎ࢢΛट౎ͱ͢Δࠃ } +

  45. Connehito Inc. 45 word2vecͷྫ - vector(‘Paris') + vector(‘Tokyo’) = vector('Japan')

    vector(‘France') ͋Δ౎ࢢΛट౎ͱ͢Δࠃ } } +

  46. Connehito Inc. 46 word2vecͷྫ - vector(‘Paris') + vector(‘Tokyo’) = vector('Japan')

    vector(‘France') ͋Δ౎ࢢΛट౎ͱ͢Δࠃ } } ౦ژ +

  47. Connehito Inc. 47 word2vecͷྫ - vector(‘Paris') + vector(‘Tokyo’) = vector('Japan')

    vector(‘France') ͋Δ౎ࢢΛट౎ͱ͢Δࠃ } } ౦ژ + =

  48. Connehito Inc. 48 word2vecͷྫ - vector(‘Paris') + vector(‘Tokyo’) = vector('Japan')

    vector(‘France') ͋Δ౎ࢢΛट౎ͱ͢Δࠃ } } ౦ژ + = }

  49. Connehito Inc. 49 word2vecͷྫ - vector(‘Paris') + vector(‘Tokyo’) = vector('Japan')

    vector(‘France') ͋Δ౎ࢢΛट౎ͱ͢Δࠃ } } ౦ژ + = }౦ژΛट౎ͱ͢Δࠃ

  50. 50

  51. 51 mamariQͰword2vecͯ͠ΈΔ

  52. None

  53. ୯ޠͷҙຯΛද͢୯ޠ Connehito Inc. 53

  54. Connehito Inc. 54 ʮ଻ಈʯin mamariQ Word: ଻ಈ Word Cosine distance

    ------------------------------------------------------------------------ ೘ҙ 0.527825 ϙίϙί 0.516658 ҧ࿨ײ 0.432082 ಈ͖ 0.430563 ͠Όͬ͘Γ 0.406297 ͙͍ͬͨ͘͢ 0.386457 ಈ͍ 0.383030 ى෬ 0.381906 ϙίο 0.377959

  55. Connehito Inc. 55 ʮυΫϯυΫϯʯin mamariQ Word: υΫϯυΫϯ Word Cosine distance

    ------------------------------------------------------------------------ ຺ଧͭ 0.454460 ϙίο 0.425674 Ͳ͘Ͳ͘ 0.425287 ϐΫο 0.418931 ಥͬுΔ 0.417948 ϐΫϐΫ 0.415464 લଆ 0.413287 ͭͬͺΔ 0.412516

  56. Connehito Inc. 56 ʮ཭ೕ৯ʯin mamariQ Word: ཭ೕ৯ Word Cosine distance

    ------------------------------------------------------------------------ ख͔ͮΈ 0.472250 ͔ͭΈ 0.445568 ॏ౬ 0.432616 ͓͔Ώ 0.425068 τΠτϨ 0.415463 ͨΜͺ࣭͘ 0.412253 ϕϏʔμϊϯ 0.393488 λϯύΫ࣭ 0.392157 ৯ࡐ 0.390477

  57. ୯ޠͱ୯ޠͷؔ܎ੑ Connehito Inc. 57

  58. Connehito Inc. 58 vector(‘ެཱ') ୯ޠͷ໘ന͍ؔ܎ੑ in mamariQ

  59. Connehito Inc. 59 - vector(‘ଉࢠ') vector(‘ެཱ') ୯ޠͷ໘ന͍ؔ܎ੑ in mamariQ

  60. Connehito Inc. 60 - vector(‘ଉࢠ') + vector(‘່’) vector(‘ެཱ') ୯ޠͷ໘ന͍ؔ܎ੑ in

    mamariQ

  61. Connehito Inc. 61 - vector(‘ଉࢠ') + vector(‘່’) = vector('ࢲཱ') vector(‘ެཱ')

    ୯ޠͷ໘ന͍ؔ܎ੑ in mamariQ

  62. 62

  63. 63 ঁͷࢠͷํ͕ࢲཱΛݕ౼͢Δʁ

  64. Connehito Inc. 64 vector(‘νϡʔϋΠ') ୯ޠͷ໘ന͍ؔ܎ੑ in mamariQ

  65. Connehito Inc. 65 - vector(‘͓฼͞Μ') vector(‘νϡʔϋΠ') ୯ޠͷ໘ന͍ؔ܎ੑ in mamariQ

  66. Connehito Inc. 66 - vector(‘͓฼͞Μ') + vector(‘͓෕͞Μ’) vector(‘νϡʔϋΠ') ୯ޠͷ໘ന͍ؔ܎ੑ in

    mamariQ

  67. Connehito Inc. 67 - vector(‘͓฼͞Μ') + vector(‘͓෕͞Μ’) = vector('Ϗʔϧ') vector(‘νϡʔϋΠ')

    ୯ޠͷ໘ന͍ؔ܎ੑ in mamariQ

  68. 68

  69. 69 உੑͷํ͕Ϗʔϧ޷͖ʁ

  70. Connehito Inc. 70 vector(‘ՄѪ͍') ୯ޠͷ໘ന͍ؔ܎ੑ in mamariQ

  71. Connehito Inc. 71 - vector(‘ଉࢠ') vector(‘ՄѪ͍') ୯ޠͷ໘ന͍ؔ܎ੑ in mamariQ

  72. Connehito Inc. 72 - vector(‘ଉࢠ') + vector(‘່’) vector(‘ՄѪ͍') ୯ޠͷ໘ന͍ؔ܎ੑ in

    mamariQ

  73. Connehito Inc. 73 - vector(‘ଉࢠ') + vector(‘່’) = vector('ՄѪ͍') vector(‘ՄѪ͍')

    ୯ޠͷ໘ന͍ؔ܎ੑ in mamariQ

  74. Connehito Inc. 74 - vector(‘ଉࢠ') + vector(‘່’) = vector('ՄѪ͍') vector(‘ՄѪ͍')

    ୯ޠͷ໘ന͍ؔ܎ੑ in mamariQ ʊਓਓਓਓਓʊ ʼɹՄѪ͍ɹʻ ʉY^Y^Y^Y
  75. None

  76. ͨͩͷ਌όΧ

  77. ೔ຊޠʹword2vecదԠ͢ΔࡍͷTips Connehito Inc. 77

  78. Connehito Inc. 78 ೔ຊޠʹword2vecదԠ͢ΔࡍͷTips

  79. Connehito Inc. • ࣙॻʹmecab-ipadic-neologdΛ࢖͏
 - Web্ͷݴޠࢿݯ͔Βಘͨ৽ޠʹରԠ 79 ೔ຊޠʹword2vecదԠ͢ΔࡍͷTips

  80. Connehito Inc. • ࣙॻʹmecab-ipadic-neologdΛ࢖͏
 - Web্ͷݴޠࢿݯ͔Βಘͨ৽ޠʹରԠ 80 ೔ຊޠʹword2vecదԠ͢ΔࡍͷTips • ۀքݻ༗ͷϫʔυ͸ͪΌΜͱొ࿥͢Δ


    - Ex. 24w3d = ೛৷͔Β24िͱ3೔໨

  81. Connehito Inc. • ࣙॻʹmecab-ipadic-neologdΛ࢖͏
 - Web্ͷݴޠࢿݯ͔Βಘͨ৽ޠʹରԠ 81 ೔ຊޠʹword2vecదԠ͢ΔࡍͷTips • ύϥϝʔλௐ੔


    - Ex. αʔϏεʹ߹Θͤͨwindow sizeΛ • ۀքݻ༗ͷϫʔυ͸ͪΌΜͱొ࿥͢Δ
 - Ex. 24w3d = ೛৷͔Β24िͱ3೔໨

  82. 82

  83. 83 ݁ہԿʹ࢖͑Δͷ͔ʁ

  84. Connehito Inc. 84 Ԡ༻ઌ

  85. Connehito Inc. • ྨٛޠݕग़ 85 Ԡ༻ઌ

  86. Connehito Inc. • ྨٛޠݕग़ 86 • QˍAίϛχϡχςΟͰ͸Qʹରͯ͠࠷΋ྑ ͍ճ౴ΛܾΊΔͱ͖ͷfeatureʹ࢖͏ͱ͔(࣮ ࡍԠ༻ͨ͠࿦จ͕͋Δ) Ԡ༻ઌ

  87. Connehito Inc. • ྨٛޠݕग़ 87 • QˍAίϛχϡχςΟͰ͸Qʹରͯ͠࠷΋ྑ ͍ճ౴ΛܾΊΔͱ͖ͷfeatureʹ࢖͏ͱ͔(࣮ ࡍԠ༻ͨ͠࿦จ͕͋Δ) •

    ͍͔ͭ͘Ծઆ͕͋ΔͷͰɺ্ख͘ߦͬͨΒ ࿦จʹͯ͠ൃ৴͍͖ͯ͠·͢ Ԡ༻ઌ

  88. ͜͜·Ͱ͕word2vecͷ͓࿩ Connehito Inc. 88

  89. Connehito Inc. • Ҏ্͕ɺword2vecΛ࢖ͬͯΈ͓ͨ࿩Ͱ͢ 89 • IVS CTO NightॳࢀՃͰϫΫϫΫͯ͠·͢ •

    ͜͏͍͏RˍDΛΈΜͳ͍ͭ΍ͬͯɺͲ͏ औΓೖΕͯΔͷ͔஌Γ͍ͨ • ͜ͷޙྑ͔ͬͨΒ͓࿩͍ͤͯͩ͘͞͞ վΊ͓ͯٓ͘͠ئ͍͠·͢ʂ

  90. Connehito Inc. • Ҏ্͕ɺword2vecΛ࢖ͬͯΈ͓ͨ࿩Ͱ͢ 90 • IVS CTO NightॳࢀՃͰϫΫϫΫͯ͠·͢ •

    ͜͏͍͏RˍDΛΈΜͳ͍ͭ΍ͬͯɺͲ͏ औΓೖΕͯΔͷ͔஌Γ͍ͨ • ͜ͷޙྑ͔ͬͨΒ͓࿩͍ͤͯͩ͘͞͞ վΊ͓ͯٓ͘͠ئ͍͠·͢ʂ ͝ਗ਼ௌ༗೉͏͍͟͝·ͨ͠ʂ