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Prerana Singhal
THE NEED FOR NATURAL
LANGUAGE PROCESSING
 No. of internet users – huge and growing
 Treasure chest of data in the form of
Natural Language
APPLICATIONS
Search
Customer SupportQ & A
Summarization
 Sentiment Analysis
NATURAL LANGUAGE
PROCESSING
 Rule based systems (since 1960s)
 Statistical Machine Learning (since late
1980s)
 Naïve Bayes, SVM, HMM, LDA, …
 Spam classifier, Google news, Google
Translate
WHY IS NLP HARD?
“Flipkart is a good
website”
(Easy)
“I didn’t receive the
product on time”
(Negation)
“Really shoddy service”
(Rare words)
“It’s gr8 to see this”
(Misspellings)
“Well played
Flipkart! You’re
giving IRCTC a
run for their
money”
(Sarcasm)
Accuracy sometimes not
good enough for production
EXCITING DEEP LEARNING RESULTS
 Amazing results, esp. in image and speech
domain
 Image Net: 6% error rate
 Facial Recognition: 97.35% accuracy
 Speech Recognition: 25% error reduction
 Handwriting Recognition (ICDAR)
IMAGE MODELS
SENSIBLE ERRORS
DEEP LEARNING FOR NLP
 Positive – Negative Sentiment Analysis
 Accuracy increase: 85% to 96%
 73% error reduction
 State-of-the-art results on various text
classification tasks (Same Model)
 Tweets, Reviews, Emails
 Beyond Text Classification
Why does it outperform
statistical models?
STATISTICAL CLASSIFIERS
RAW DATA
Flipkart! You need to improve your delivery
FEATURE ENGINEERING
 Functions which transform input (raw) data into a
feature space
 Discriminative – for decision boundary
 Feature engineering is painful
 Deep Neural Networks: Identify the features
automatically
Neural Networks
DEEP NEURAL NETWORKS
Higher layers form higher levels of abstractions.
DEEP NEURAL NETWORKS
Unsupervised pre-training
DEEP LEARNING FOR NLP
 Why Deep Learning?
 Problems with applying deep-learning to
natural language
PROBLEMS WITH STATISTICAL
MODELS
BAG OF WORDS
“FLIPKART IS
BETTER THAN
AMAZON”
PROBLEMS WITH STATISTICAL
MODELS
 Word ordering information lost
 Data sparsity
 Words as atomic symbols
 Very hard to find higher level features
 Features other than BOW
HOW TO ENCODE THE
MEANING OF A WORD?
 Wordnet: Dictionary of synonyms
 Synonyms: Adept, expert, good, practiced,
proficient, skillful
WORD EMBEDDINGS: THE FIRST
BREAKTHROUGH
NEURAL LANGUAGE MODEL
WORD EMBEDDINGS:
VISUALIZATIONS
CAPTURE RELATIONSHIPS
WORD EMBEDDING: VISUALIZATIONS
WORD EMBEDDING: VISUALIZATIONS
WORD EMBEDDING:
VISUALIZATIONS
 Trained in a completely unsupervised way
 Reduce data sparsity
 Semantic Hashing
 Appear to carry semantic information
about the words
 Freely available for Out of Box usage
COMPOSITIONALITY
 How do we go beyond words (sentences and
paragraphs)?
 This turns out to be a very hard problem
 Simple Approaches
 Word Vector Averaging
 Weighted Word Vector Averaging
Deep Learning for Natural Language Processing
CONVOLUTIONAL NEURAL
NETWORKS
 Excellent feature extractors in image
 Features are detected regardless of position in
image
 NLP Almost from Scratch: Collobert et al 2011
 First applied CNN for NLP
Deep Learning for Natural Language Processing
CNN FOR TEXT
-0.33
0.56
0.98
-0.13
-0.81
-0.01
0.17
0.64
-0.16
0.97
0.99
0.90
-0.23
0.16
0.68
-0.33
0.56
0.98
-0.13
-0.81
-0.01
0.17
0.64
-0.16
0.97
0.99
0.90
-0.23
0.16
0.68
0.46 0.04 -0.09 Composition
-0.33
0.56
0.98
-0.13
-0.81
-0.01
0.17
0.64
-0.16
0.97
0.99
0.90
-0.23
0.16
0.68
Weight Matrix
(3 x 9)
[-0.33 0.56 0.98 -0.13 -0.81 -0.01 0.17 0.64 -0.16]
[-0.33 0.56 0.98 -0.13 -0.81
-0.01 0.17 0.64 -0.16]
[0.46 0.04 -0.09]
0.46 0.04 -0.09
-0.33
0.56
0.98
-0.13
-0.81
-0.01
0.17
0.64
-0.16
0.97
0.99
0.90
-0.23
0.16
0.68
-0.57 0.81 0.25
0.46
0.04
-0.09
-0.33
0.56
0.98
-0.13
-0.81
-0.01
0.17
0.64
-0.16
0.97
0.99
0.90
-0.23
0.16
0.68
-0.18 0.26 0.40
-0.57
0.81
0.25
0.46
0.04
-0.09
-0.33
0.56
0.98
-0.13
-0.81
-0.01
0.17
0.64
-0.16
0.97
0.99
0.90
-0.23
0.16
0.68
-0.57
0.81
0.25
0.46
0.04
-0.09
-0.13
0.26
0.40
-0.33
0.56
0.98
-0.13
-0.81
-0.01
0.17
0.64
-0.16
0.97
0.99
0.90
-0.23
0.16
0.68
-0.57
0.81
0.25
0.46
0.04
-0.09
-0.13
0.26
0.40
0.46
0.81
0.40
-0.33
0.56
0.98
-0.13
-0.81
-0.01
0.17
0.64
-0.16
0.97
0.99
0.90
-0.23
0.16
0.68
-0.57
0.81
0.25
0.46
0.04
-0.09
-0.13
0.26
0.40
0.46
0.81
0.40
Neutral
DEMYSTIFYING MAX POOLING
 Finds the most important part(s) of sentence
CNN FOR TEXT
 Window sizes: 3,4,5
 Static mode
 Non Static mode
 Multichannel mode
 Multiclass Classification
RESULTS
Dataset Source Labels Statistical
Models
CNN
Flipkart Twitter
Sentiment
Twitter Pos, Neg 85% 96%
Flipkart Twitter
Sentiment
Twitter Pos, Neg, Neu 76% 89%
Fine grained
sentiment in Emails
Emails Angry, Sad, Complaint,
Request
55% 68%
SST2 Movie
Reviews
Pos, Neg 79.4% 87.5%
SemEval Task 4 Restaurant
Reviews
food / service / ambience /
price / misc
88.5% 89.6%
SENTIMENT: ANECDOTES
DRAWBACKS & LEARNINGS
 Computationally Expensive
 How to scale training?
 How to scale prediction?
 Libraries for Deep Learning
 Theano
 PyLearn2
 Torch
“I THINK YOU SHOULD BE MORE EXPLICIT HERE IN STEP TWO”
OPEN SOURCED
 https://github.com/flipkart-incubator/optimus
BEYOND TEXT CLASSIFICATION
 Text Classification covers a lot of NLP
problems (or problems can be reduced to it)
 Word Embedding
 Unsupervised Learning
 Sequence Learning
 RNN, LSTM
RECURRENT MODELS
 RNNs, LSTMs
 Machine Translation, Chat, Classification
ANY QUESTIONS ?

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Deep Learning for Natural Language Processing

Notes de l'éditeur

  1. Information Extraction Personalization….
  2. Information Extraction Personalization….
  3. Information Extraction Personalization…. Very hard problem for computers Science of deriving meaning from Natural Language Still, not enough good systems in production
  4. Information Extraction Personalization….
  5. Loosely inspired by what (little) we know about the biological brain
  6. Why image is hard?
  7. Information Extraction Personalization….
  8. Information Extraction Personalization….
  9. Real life: 1000sof D space
  10. Real life: 1000sof D space
  11. Information Extraction Personalization….
  12. Elaborate more on pain of feature engineeing Hundreds of thousands of features in real life
  13. Information Extraction Personalization….
  14. Information Extraction Personalization….
  15. Put unsup chart
  16. How to solve classification problems and getting semantic representations of Natural Language using DL? Revise
  17. Information Extraction Personalization….
  18. Bigram trigram
  19. Manual feature engineering disadvantages – not generic POS Tags Brown clusters Negation Manually created lexicons ….
  20. Mention LSA
  21. Cat and dog have lot of semantic similarity compared to say cat and ambulance
  22. Information Extraction Personalization….
  23. Information Extraction Personalization….
  24. Information Extraction Personalization….
  25. Information Extraction Personalization….
  26. Information Extraction Personalization….
  27. Trained on google news dataset
  28. Information Extraction Personalization….
  29. Information Extraction Personalization….
  30. Information Extraction Personalization….
  31. Information Extraction Personalization….
  32. Information Extraction Personalization….
  33. Information Extraction Personalization….
  34. Information Extraction Personalization….
  35. Information Extraction Personalization….
  36. Information Extraction Personalization….
  37. Information Extraction Personalization….
  38. Information Extraction Personalization….
  39. Information Extraction Personalization….