The document discusses the vital role of Natural Language Processing (NLP) in handling the growing data generated online, highlighting various applications like sentiment analysis and customer support. It details the evolution of NLP techniques from rule-based systems to deep learning approaches, emphasizing the effectiveness of deep neural networks in improving accuracy and feature extraction. Additionally, it addresses challenges faced in NLP, such as data sparsity and the complexities of word meaning, while showcasing how advanced models like CNNs and RNNs enhance text classification and understanding.

1. Prerana Singhal

2. THE NEED FOR NATURAL
LANGUAGE PROCESSING
 No. of internet users – huge and growing
 Treasure chest of data in the form of
Natural Language

3. APPLICATIONS
Search
Customer SupportQ & A
Summarization

4.  Sentiment Analysis

5. 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

6. WHY IS NLP HARD?
“Flipkart is a good
website”
(Easy)

7. “I didn’t receive the
product on time”
(Negation)

8. “Really shoddy service”
(Rare words)

9. “It’s gr8 to see this”
(Misspellings)

10. “Well played
Flipkart! You’re
giving IRCTC a
run for their
money”
(Sarcasm)

11. Accuracy sometimes not
good enough for production

12. 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)

13. IMAGE MODELS

14. SENSIBLE ERRORS

15. 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

16. Why does it outperform
statistical models?

17. STATISTICAL CLASSIFIERS

18. RAW DATA
Flipkart! You need to improve your delivery

19. 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

20. Neural Networks

21. DEEP NEURAL NETWORKS
Higher layers form higher levels of abstractions.

22. DEEP NEURAL NETWORKS
Unsupervised pre-training

23. DEEP LEARNING FOR NLP
 Why Deep Learning?
 Problems with applying deep-learning to
natural language

24. PROBLEMS WITH STATISTICAL
MODELS

25. BAG OF WORDS
“FLIPKART IS
BETTER THAN
AMAZON”

26. 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

27. HOW TO ENCODE THE
MEANING OF A WORD?
 Wordnet: Dictionary of synonyms
 Synonyms: Adept, expert, good, practiced,
proficient, skillful

28. WORD EMBEDDINGS: THE FIRST
BREAKTHROUGH

29. NEURAL LANGUAGE MODEL

30. WORD EMBEDDINGS:
VISUALIZATIONS

31. CAPTURE RELATIONSHIPS

32. WORD EMBEDDING: VISUALIZATIONS

33. WORD EMBEDDING: VISUALIZATIONS

34. 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

35. 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

37. 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

39. CNN FOR TEXT

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

41. -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

42. -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

43. -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

44. -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

45. -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

46. -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

47. -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

48. DEMYSTIFYING MAX POOLING
 Finds the most important part(s) of sentence

49. CNN FOR TEXT
 Window sizes: 3,4,5
 Static mode
 Non Static mode
 Multichannel mode
 Multiclass Classification

50. 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%

51. SENTIMENT: ANECDOTES

52. DRAWBACKS & LEARNINGS
 Computationally Expensive
 How to scale training?
 How to scale prediction?
 Libraries for Deep Learning
 Theano
 PyLearn2
 Torch

53. “I THINK YOU SHOULD BE MORE EXPLICIT HERE IN STEP TWO”

54. OPEN SOURCED
 https://github.com/flipkart-incubator/optimus

55. 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

56. RECURRENT MODELS
 RNNs, LSTMs
 Machine Translation, Chat, Classification

57. ANY QUESTIONS ?