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Chat Bot Made by Chainer
Chainer is a Neural Network Framework
PyCon JP 2016
Masaya Ogushi
1
Attention
I will not show any Mathematical formula
If you understand the machine learning
model, I recommend to read the p...
Agenda
:
Self Introduction
Dialogue Value
Character of the Bot
System
Feature Plan
3
Chat Bot
Choose
the Topic
Understand
...
Self Introduction
4
Self Introduction
Name:Masaya Ogushi
@SnowGushiGit
PORT. Inc
Web Development
Research and Development team
Tech-Circle sta...
Self Introduction
We’re Hiring!!
https://www.theport.jp/recruit/information/
6
Self Introduction
Free Consulting for your job search
https://port-recruitment.jp/
7
Dialogue Value
8
Agenda
:
Self Introduction
Dialogue Value
Character of the Bot
System
Feature Plan
9
Chat Bot
Choose
the Topic
Understand
...
Dialogue Value
Dialogue Value
Continuously
Interactive
New User Experiences
10
Dialogue Value
Continuously
It is possible to use the
prior conversation’s
information
11
I really love to
play the tennis...
Dialogue Value
Interactive
It is possible to react to
new information
12
I found the
delicious sweets
I am on a diet
Don’t...
Dialogue Value
New User Experiences
Character
13
Example
Dialogue Value
Dialogue Value
continuously
Interactive
New User Experiences
14
Need Dialogue
Data
Possible to achieve
even...
Character of the Bot
15
Agenda
:
Self Introduction
Dialogue Value
Character of the Bot
System
Feature Plan
16
Chat Bot
Choose
the Topic
Understand...
Which Characters looks smart ?
17
Character of the Bot
Which Characters would you like to talk with?
18
Character of the Bot
Which looks and character is a big gap ?
19
Character of the Bot
I read the
“Statistical Machine
Translation”
I read the
“...
Character of the Bot
I recommend
Matsuya
Do you know any
good part time
jobs
No, something
more suited to
me
How about a
s...
Character of the Bot
Improve the new use
experiences
Decreasing the expected
value of the answer and
Became easer to talk ...
Character of the Bot
Preparing the sentences
for each character is costly
We have to change a little
in the same sentences...
23
Character of the Bot
We need Many scenario writers
Character of the Bot
We would like to change
the Character but not
change the contents
24
あなたは食べたパン
の数を
把握されていますか?
お前は食ったパ...
Character of the Bot
25
Woman
Fat Man
Steward
Do you remember the
number of the breads ?
Add the
Character
Character of the Bot
NeuralStoryTeller
Add the Character into the normal sentences。Add the romantic elements below
26
27
Character of the Bot
It is possible to apply to a variety of situations, if we
prepare the characters sentences
28
Character of the Bot
I’m sorry.
I can’t implement this characters function.
Agenda
:
Self Introduction
Dialogue Value
Character of the Bot
System
Feature Plan
29
Chat Bot
Choose
the Topic
Understand...
System
30
Agenda
:
System Architecture
Dialogue Interface is Slack
Prepare the conversation data form Twitter
Pre training use the W...
Agenda
:32
Chat Bot
Choose
the Topic
Understand
Contents Control
Dialogue
Generate
Answer
Answer
Candidates
Neural Network...
System
Choose the Topic
Conversation contents is changed by the someone
33
Which is
more important
to you
me or your job
P...
System
Choose the Topic
Word Net
Data set is the grouped into set of cognitive synonyms, each
expressing a distinct concep...
35
System
Many concept. 57238 concepts
It is difficult to prepare the data.
36
System
Grouping the concepts
System
The way of grouping
Mapping the concept space
Grouping by distance
37
cat
cat
cat
Tiger
System
Mapping the concept space
38
Facebook
Twitter
Close??
We could not understand
the distance comparing
each words
We ...
System
Mapping the concept space
Entity Linking
Mapping the Keyword to the Knowledge space
39
Facebook
Twitter
Close??
Kno...
System
Choose the Topic
Mapping the concept to the knowledge space
Japanese WikiPedia Entity Vector !!!
Vector representat...
System
Choose the Topic
Synonym get the vector by the WikiPedia Entity Vector
41
cat:[0.2, 0.3, 0.4…] dog:[0.3, 0.4, 0.5…]
42
System
Measure the Distance
System
Measuring the Concept Distance
43
Choose the appropriate
measure for the
distance in mapping
space
If we make a mis...
44
System
I used cosine similarity to measure the
vector distance
System
Choose the Topic
Synonym get the vector by the WikiPedia Entity Vector
45
Cat:[0.2, 0.3, 0.4…] Dog:[0.3, 0.4, 0.5…]...
46
System
Many Concepts yet
System
Choose the Topic
Add the Unknown words of the WordNet from the Wikipedia Entity Vector.
47
Black Cat
White Cat
:
ca...
System
Choose the Topic
Calculate the each concept Average vector
48
Black Cat:[0.2, 0.3, 0.4…]
White Cat:[0.1, 0.3, 0.…]
...
System
Choose the Topic
If the average vector is close to each concept, group them by concept
49
Black Cat:[0.2, 0.3, 0.4…...
50
System
Many concepts yet(20000 concepts)
System
Choose the Topic
Choose the concept from over the 1000 words. It is easy to match the phrase.
51
Black Cat
White Ca...
52
System
76 concepts
(Attention:I didn’t use the all WikiPedia Entity Vectors)
System
Choose the Topic
The way of the choosing the dialogue
Choose the each concept by the word match rate
53
Where
can I...
Agenda
:54
Chat Bot
Choose
the Topic
Understand
Contents Control
Dialogue
Generate
Answer
Answer
Candidates
Neural Network...
55
System
All parts made by the Neural Network
(Attention:I might be the best way)
56
System
Why Neural Network?
I will explain how to apply the Neural Network to
Natural Language Processing
Dialogue Value
Value of the Neural Network
Expression
Continuously
Focus
57
Dialogue Value
Value of the Neural Network
Expression
Continuously
Focus
58
59
System
Mapping natural language to the vector space using the Bag of
words
(Prepare the Dictionary and Count the word i...
60
System
It only considers words.
61
System
Deep Learning is an efficient method for learning high-quality
distributed vector representations that capture a ...
Dialogue Value
Value of the Neural Network
Expression
Continuously
Focus
62
63
System
太郎 さん こんにちは
Phrase is important for Continuously
Recurrent Neural Network is possible to consider the
Continuous...
Dialogue Value
Value of the Neural Network
Expression
Continuously
Focus
64
65
System
+
太郎 さん こんにちは
Focus is important for important phrasing.
A Attention Model(Neural Network) considers which are
t...
System
Value of the Neural Network
Expression
Continuously
Focus
66
Which is more
important
to you
me or your job
Please t...
67
System
How to implement a Neural Network
68
System
This is a Dialogue Model
太郎 さん こんにちは
こんにちは<EOS>
+Encoder
Decoder
69
System
Mapping the Phrases to a neural network space.
The middle layer express a neural network space.
太郎 さん こんにちは
太郎:1...
70
System
Continuously learn from phrases
0
0
0
0
1
:
0 output
layer
さん
こんにちはhidden
layer
太郎の時
の
隠れ層
Transform Matrix
Copy...
71
System
The input sentence is reversed.
The first word is the most important.
太郎 さん こんにちは
72
System
Forward Information and Reverse Information are
Convolution
+
Encoder
太郎 さん こんにちは
73
System
Generating the phrases by the Convolution information
太郎 さん こんにちは
+Encoder
こんにちは
Decoder
74
System
It is consider the continuously Generating the Phrases
太郎 さん こんにちは
こんにちは<EOS>
+Encoder
Decoder
75
System
The Value of this model
Expression
Continuously
Focus
Agenda
:76
Chat Bot
Choose
the Topic
Understand
Contents Control
Dialogue
Generate
Answer
Answer
Candidates
Neural Network...
77
System
How to know if it’s a question or not
System
It is very simple to decide
Is there a question mark (?)
If you interested in detecting questions, I recommend you ...
79
System
Demonstration!!
https://youtu.be/ulICnU2f2Po
Agenda
:
Self Introduction
Dialogue Value
Character of the Bot
System
Feature Plan
80
Chat Bot
Choose
the Topic
Understand...
Feature Plan
81
Feature Plan
Prepare the enough test
Not Enough test code
Evaluation
F measure
Apply the latest Chainer
I hear the Trainer...
Conclusion
Word Net is a Concept Dataset
It is possible to find other data which express the concept
Mapping words to Vecto...
Conclusion
We’re Hiring!!
https://www.theport.jp/recruit/information/
84
Reference
• Chainerで学習した対話用のボットをSlackで使用+Twitterから学習データを取得してファインチューニン
• http://qiita.com/GushiSnow/items/79ca7deeb976f5012...
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Chat bot made by the chainer

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Chat bot made by the chainer
chainer is the neural network framework

Japanese sentence remove. I don't know the reason why removing the Japanese sentence

This presentation material is the Pycon 2016

*******************************************
大串 正矢
Ogushi Masaya
Twitter:https://twitter.com/SnowGushiGit
Qiita:http://qiita.com/GushiSnow
Github:https://github.com/SnowMasaya
*******************************************

Published in: Data & Analytics
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Chat bot made by the chainer

  1. 1. Chat Bot Made by Chainer Chainer is a Neural Network Framework PyCon JP 2016 Masaya Ogushi 1
  2. 2. Attention I will not show any Mathematical formula If you understand the machine learning model, I recommend to read the paper in the last page 2
  3. 3. Agenda : Self Introduction Dialogue Value Character of the Bot System Feature Plan 3 Chat Bot Choose the Topic Understand Contents Control Dialogue Generate Answer Answer Candidates Neural Network1 Understand Contents Control Dialogue Generate Answer Neural Network 2 Question Question Answer
  4. 4. Self Introduction 4
  5. 5. Self Introduction Name:Masaya Ogushi @SnowGushiGit PORT. Inc Web Development Research and Development team Tech-Circle staff machine learning, Natural Language Processing, Crawler Dev, Automatic Infrastructure Construction, parallel processing, SearchFunction 5
  6. 6. Self Introduction We’re Hiring!! https://www.theport.jp/recruit/information/ 6
  7. 7. Self Introduction Free Consulting for your job search https://port-recruitment.jp/ 7
  8. 8. Dialogue Value 8
  9. 9. Agenda : Self Introduction Dialogue Value Character of the Bot System Feature Plan 9 Chat Bot Choose the Topic Understand Contents Control Dialogue Generate Answer Answer Candidates Neural Network1 Understand Contents Control Dialogue Generate Answer Neural Network 2 Question Question Answer
  10. 10. Dialogue Value Dialogue Value Continuously Interactive New User Experiences 10
  11. 11. Dialogue Value Continuously It is possible to use the prior conversation’s information 11 I really love to play the tennis Chat Bot That’s sounds great Well, a friend of mine owns a sports shop and is looking for help. find the part time job candidates Yeah well I looking for the part time job Do you know the any good ones?
  12. 12. Dialogue Value Interactive It is possible to react to new information 12 I found the delicious sweets I am on a diet Don’t say such a things during the I’m on a diet This food is a good for losing the weight Really ??? Chat Bot
  13. 13. Dialogue Value New User Experiences Character 13 Example
  14. 14. Dialogue Value Dialogue Value continuously Interactive New User Experiences 14 Need Dialogue Data Possible to achieve even without dialogue data
  15. 15. Character of the Bot 15
  16. 16. Agenda : Self Introduction Dialogue Value Character of the Bot System Feature Plan 16 Chat Bot Choose the Topic Understand Contents Control Dialogue Generate Answer Answer Candidates Neural Network1 Understand Contents Control Dialogue Generate Answer Neural Network 2 Question Question Answer
  17. 17. Which Characters looks smart ? 17 Character of the Bot
  18. 18. Which Characters would you like to talk with? 18 Character of the Bot
  19. 19. Which looks and character is a big gap ? 19 Character of the Bot I read the “Statistical Machine Translation” I read the “Statistical Machine Translation”
  20. 20. Character of the Bot I recommend Matsuya Do you know any good part time jobs No, something more suited to me How about a sweets shop ? Sounds Good Character is very important 20 Food Shop Maybe Sweets are the better Looks Funny Uhhh.. He only think about food. You understand it better than I expected Learning allowance Surprise Chat Bot
  21. 21. Character of the Bot Improve the new use experiences Decreasing the expected value of the answer and Became easer to talk to Image of the Icon Conversation of the Bot 21 Cognitive Science Expect value of the answer:High Easy to talk:Low Expect value of the answer:Low Easy to talk:High Feel free to talk to me Feel free to talk to me Talk it
  22. 22. Character of the Bot Preparing the sentences for each character is costly We have to change a little in the same sentences *The Example uses the different levels of politeness in Japanese, the nuances of which are hard to translate into English 22 あなたは食べたパン の数を 把握されていますか? お前は食ったパンの 数を 覚えているか? あなたは食べたパン の数を 覚えているの?
  23. 23. 23 Character of the Bot We need Many scenario writers
  24. 24. Character of the Bot We would like to change the Character but not change the contents 24 あなたは食べたパン の数を 把握されていますか? お前は食ったパンの 数を 覚えているか? あなたは食べたパン の数を 覚えているの?
  25. 25. Character of the Bot 25 Woman Fat Man Steward Do you remember the number of the breads ? Add the Character
  26. 26. Character of the Bot NeuralStoryTeller Add the Character into the normal sentences。Add the romantic elements below 26
  27. 27. 27 Character of the Bot It is possible to apply to a variety of situations, if we prepare the characters sentences
  28. 28. 28 Character of the Bot I’m sorry. I can’t implement this characters function.
  29. 29. Agenda : Self Introduction Dialogue Value Character of the Bot System Feature Plan 29 Chat Bot Choose the Topic Understand Contents Control Dialogue Generate Answer Answer Candidates Neural Network1 Understand Contents Control Dialogue Generate Answer Neural Network 2 Question Question Answer
  30. 30. System 30
  31. 31. Agenda : System Architecture Dialogue Interface is Slack Prepare the conversation data form Twitter Pre training use the Wikipedia Data and the Dialogue Breakdown Collection Choose the TOPIC by using the WordNet and WikiPedia Entity Vector Dialogue model made by Chainer Question and Answer functionally uses Elasticsearch 31 Chat Bot Choose the Topic Understand Contents Control Dialogue Generate Answer Answer Candidates Neural Network1 Understand Contents Control Dialogue Generate Answer Neural Network 2 Question Question Answer WordNet WikiPediaVector
  32. 32. Agenda :32 Chat Bot Choose the Topic Understand Contents Control Dialogue Generate Answer Answer Candidates Neural Network1 Understand Contents Control Dialogue Generate Answer Neural Network 2 Question Question Answer WordNet WikiPediaVector Choose the Topic
  33. 33. System Choose the Topic Conversation contents is changed by the someone 33 Which is more important to you me or your job Please tell me where you bought your clothes Please give me a money Boy Friend Young Sister Father
  34. 34. System Choose the Topic Word Net Data set is the grouped into set of cognitive synonyms, each expressing a distinct concept 34 Scottish hold Black cat Orange Cat Cat
  35. 35. 35 System Many concept. 57238 concepts It is difficult to prepare the data.
  36. 36. 36 System Grouping the concepts
  37. 37. System The way of grouping Mapping the concept space Grouping by distance 37 cat cat cat Tiger
  38. 38. System Mapping the concept space 38 Facebook Twitter Close?? We could not understand the distance comparing each words We have to map the word to space, which makes it possible to measure the distance
  39. 39. System Mapping the concept space Entity Linking Mapping the Keyword to the Knowledge space 39 Facebook Twitter Close?? Knowledge Space Facebook Twitter SNS
  40. 40. System Choose the Topic Mapping the concept to the knowledge space Japanese WikiPedia Entity Vector !!! Vector representations of Words and WikiPedia(Knowldge) (Wikipedia is the called the Entity) 40
  41. 41. System Choose the Topic Synonym get the vector by the WikiPedia Entity Vector 41 cat:[0.2, 0.3, 0.4…] dog:[0.3, 0.4, 0.5…]
  42. 42. 42 System Measure the Distance
  43. 43. System Measuring the Concept Distance 43 Choose the appropriate measure for the distance in mapping space If we make a mistake choosing the measuring of the distance. It looks yellow is close Light blue is closer than yellow
  44. 44. 44 System I used cosine similarity to measure the vector distance
  45. 45. System Choose the Topic Synonym get the vector by the WikiPedia Entity Vector 45 Cat:[0.2, 0.3, 0.4…] Dog:[0.3, 0.4, 0.5…] Cosine Similarity
  46. 46. 46 System Many Concepts yet
  47. 47. System Choose the Topic Add the Unknown words of the WordNet from the Wikipedia Entity Vector. 47 Black Cat White Cat : calico cat : CatWikipedia Entity Vector Close the Cosine Similarity Add the Unknown words Word Net
  48. 48. System Choose the Topic Calculate the each concept Average vector 48 Black Cat:[0.2, 0.3, 0.4…] White Cat:[0.1, 0.3, 0.…] : Cat Shiba:[0.1, 0.3, 0.4…] Tosa:[0.1, 0.2, 0.…] : Dog Average Vector Average Vector
  49. 49. System Choose the Topic If the average vector is close to each concept, group them by concept 49 Black Cat:[0.2, 0.3, 0.4…] White Cat:[0.1, 0.3, 0.…] : Cat Shiba:[0.1, 0.3, 0.4…] Tosa:[0.1, 0.2, 0.…] : Dog Average Vector Average Vector grouping the each concept
  50. 50. 50 System Many concepts yet(20000 concepts)
  51. 51. System Choose the Topic Choose the concept from over the 1000 words. It is easy to match the phrase. 51 Black Cat White Cat : Cat Shiba Tosa : Dog swan duck : Bird koala Koala Choosing the Concept
  52. 52. 52 System 76 concepts (Attention:I didn’t use the all WikiPedia Entity Vectors)
  53. 53. System Choose the Topic The way of the choosing the dialogue Choose the each concept by the word match rate 53 Where can I buy cute clothes ? Boy Friend Cool Nice guy : Young Sister Cute Clothes : Father money gentle : Calculate the word match rate
  54. 54. Agenda :54 Chat Bot Choose the Topic Understand Contents Control Dialogue Generate Answer Answer Candidates Neural Network1 Understand Contents Control Dialogue Generate Answer Neural Network 2 Question Question Answer WordNet WikiPediaVector Understand Contents Control Dialogue Generate the Answer
  55. 55. 55 System All parts made by the Neural Network (Attention:I might be the best way)
  56. 56. 56 System Why Neural Network? I will explain how to apply the Neural Network to Natural Language Processing
  57. 57. Dialogue Value Value of the Neural Network Expression Continuously Focus 57
  58. 58. Dialogue Value Value of the Neural Network Expression Continuously Focus 58
  59. 59. 59 System Mapping natural language to the vector space using the Bag of words (Prepare the Dictionary and Count the word in the dictionary) Low High Word Phrase sentenceExpression I show am me your you … when are 1, 0, 0, 0, 0, 0, … 0, 0I am Shota I show am me your you … when are 0, 0, 1, 0, 0, 0, … 0, 0 I show am me your you … when are 0, 0, 0, 0, 0, 0, … 0, 0 It is rate time to use it, but over the million wordsData
  60. 60. 60 System It only considers words.
  61. 61. 61 System Deep Learning is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships Low High Word Phrase sentenceExpression I am Shota Distributed representations of words in a vector space by the Deep leaning Data Deep Learning 0.5, 0.0, 1.0, 1.0, 0.3, 0.0 0.5, 0.0, 1.0, 1.0, 0.0, 0.0 0.5, 0.0, 1.0, 0.5, 0.3, 0.0
  62. 62. Dialogue Value Value of the Neural Network Expression Continuously Focus 62
  63. 63. 63 System 太郎 さん こんにちは Phrase is important for Continuously Recurrent Neural Network is possible to consider the Continuously
  64. 64. Dialogue Value Value of the Neural Network Expression Continuously Focus 64
  65. 65. 65 System + 太郎 さん こんにちは Focus is important for important phrasing. A Attention Model(Neural Network) considers which are the focus words
  66. 66. System Value of the Neural Network Expression Continuously Focus 66 Which is more important to you me or your job Please tell me where you bought your clothes Please give me money Boy Friend Young Sister Father
  67. 67. 67 System How to implement a Neural Network
  68. 68. 68 System This is a Dialogue Model 太郎 さん こんにちは こんにちは<EOS> +Encoder Decoder
  69. 69. 69 System Mapping the Phrases to a neural network space. The middle layer express a neural network space. 太郎 さん こんにちは 太郎:1
 さん:0
 こんにちは:0 : 太郎:1
 さん:0
 こんにちは:0 :
  70. 70. 70 System Continuously learn from phrases 0 0 0 0 1 : 0 output layer さん こんにちはhidden layer 太郎の時 の 隠れ層 Transform Matrix Copy the past value 太郎 さん こんにちは
  71. 71. 71 System The input sentence is reversed. The first word is the most important. 太郎 さん こんにちは
  72. 72. 72 System Forward Information and Reverse Information are Convolution + Encoder 太郎 さん こんにちは
  73. 73. 73 System Generating the phrases by the Convolution information 太郎 さん こんにちは +Encoder こんにちは Decoder
  74. 74. 74 System It is consider the continuously Generating the Phrases 太郎 さん こんにちは こんにちは<EOS> +Encoder Decoder
  75. 75. 75 System The Value of this model Expression Continuously Focus
  76. 76. Agenda :76 Chat Bot Choose the Topic Understand Contents Control Dialogue Generate Answer Answer Candidates Neural Network1 Understand Contents Control Dialogue Generate Answer Neural Network 2 Question Question Answer WordNet WikiPediaVector Question and Answer Function
  77. 77. 77 System How to know if it’s a question or not
  78. 78. System It is very simple to decide Is there a question mark (?) If you interested in detecting questions, I recommend you read the paper below Li, Baichuan, et al. "Question identification on twitter." Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, 2011. 78 Where can I buy cute clothes ? Understand Contents Control Dialogue Generate Answer Neural Network1 Question Answer Please tell me where I can find cute clothes
  79. 79. 79 System Demonstration!! https://youtu.be/ulICnU2f2Po
  80. 80. Agenda : Self Introduction Dialogue Value Character of the Bot System Feature Plan 80 Chat Bot Choose the Topic Understand Contents Control Dialogue Generate Answer Answer Candidates Neural Network1 Understand Contents Control Dialogue Generate Answer Neural Network 2 Question Question Answer
  81. 81. Feature Plan 81
  82. 82. Feature Plan Prepare the enough test Not Enough test code Evaluation F measure Apply the latest Chainer I hear the Trainer function is good Rule base and Neural Network NeuralStoryTeller Add the character 82
  83. 83. Conclusion Word Net is a Concept Dataset It is possible to find other data which express the concept Mapping words to Vector space using Wikipedia Entity Vectors We make the Vector spaces using our own data set Hybrid function (Neural Network and Rule based) Please search github for “Chainer Slack Twitter” Please give me a star I prepare the Docker Container please search for “Docker hub Chainer-Slack-Twitter-Dialogue” 83
  84. 84. Conclusion We’re Hiring!! https://www.theport.jp/recruit/information/ 84
  85. 85. Reference • Chainerで学習した対話用のボットをSlackで使用+Twitterから学習データを取得してファインチューニン • http://qiita.com/GushiSnow/items/79ca7deeb976f50126d7 • WordNet • http://nlpwww.nict.go.jp/wn-ja/ • 日本語 Wikipedia エンティティベクトル • http://www.cl.ecei.tohoku.ac.jp/~m-suzuki/jawiki_vector/ • PAKUTASO • https://www.pakutaso.com/ • Luong, Minh-Thang, Hieu Pham, and Christopher D. Manning. "Effective approaches to attention-based neural machine translation." arXiv preprint arXiv:1508.04025 (2015). • Rush, Alexander M., Sumit Chopra, and Jason Weston. "A neural attention model for abstractive sentence summarization." arXiv preprint arXiv:1509.00685 (2015). • Tech Circle #15 Possibility Of BOT • http://www.slideshare.net/takahirokubo7792/tech-circle-15-possibility-of-bot • Generating Stories about Images • https://medium.com/@samim/generating-stories-about-images-d163ba41e4ed#.h80qhbd54 • 二つの文字列の類似度 • http://d.hatena.ne.jp/ktr_skmt/20111214/1323835913 • Li, Baichuan, et al. "Question identification on twitter." Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, 2011. • 音源:スカイウォーキング • http://dova-s.jp/bgm/download5052.html • 音源:get into the rhythm • http://dova-s.jp/bgm/download5145.html • 構文解析 • http://qiita.com/laco0416/items/b75dc8689cf4f08b21f6 85

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