Deep Learning for Information Retrieval

1. @graphiﬁc Roelof Pieters Guest Lecture: Deep Learning for Informa8on Retrieval 28 April 2015 www.csc.kth.se/~roelof/ roelof@kth.se roelof@graph-technologies.com Gve Systems Graph Technologies R&D DD2476 Search Engines and Information Retrieval Systems https://www.kth.se/social/course/DD2476/ slides online at   h4p://www.slideshare.net/roelofp/deep-‐learning-‐for-‐informa=on-‐retrieval

2. 2 About Me • (-10y) CS dropout (Amsterdam Technical Univ.) • (2y) Msc Social Anthropology, Stockholm University • Current: PhD candidate at KTH/CSC with focus on: • Deep Learning for Natural Language Processing (Distributed Semantics) • Graph-based approaches for Knowledge Representation • Multi-modal models • Current: Data Science Consultant at Graph Technologies RD & Gve-Systems • Recommender Systems • Deep Learning • Realtime Graph-based Search Engines

3. 3 Information Retrieval (IR) - Hedvig Kjellström, lecture 1

4. 4 Data landscape is changing 1. Amount of digital data is growing at increasing rate (IOT, digitalization, wearables, phones/ tablets) 2. Data types are shifting as well: 1. from text to audio-visual 2. from professional to personal/social (social media) 3. from semi-structured to unstructured

5. [Jussi Karlgren, NLP Sthlm Meetup 2014]

6. 6 Data landscape is changing Triple V’s of Big Data: 1. Volume 2. Velocity 3. Variety

7. 7 Making sense of Data Typical ML Regression

8. 8 Making sense of Data Neural NetTypical ML Regression

9. Degrees of Complexity 9 perceptron demo

10. Neural Net 10 (ﬁgure from Lior Rokach, Ben-Gurion University)

15. Neural Net 15 multilayer nn demo

16. Deep Learning ?? 16

17. Deep Learning ?? 17 • Learning multiple layers • “Back propagation” • Can “theoretically” learn any function! Prior to 2006: • Very slow and ineﬃcient • SVMs, random forests, etc. SOTA

18. 18 2006+: the 3 Deep Learning Conspirators

19. 19

20. 20

21. — Andrew Ng “I’ve worked all my life in Machine Learning, and I’ve never seen one algorithm knock over benchmarks like Deep Learning” Deep Learning: Why? 21

22. Different Levels of Abstraction 22

23. Hierarchical Learning • Natural progression from low level to high level structure as seen in natural complexity Different Levels of Abstraction Feature Representation 23

24. Hierarchical Learning • Natural progression from low level to high level structure as seen in natural complexity • Easier to monitor what is being learnt and to guide the machine to better subspaces Different Levels of Abstraction Feature Representation 24

25. Hierarchical Learning • Natural progression from low level to high level structure as seen in natural complexity • Easier to monitor what is being learnt and to guide the machine to better subspaces • A good lower level representation can be used for many distinct tasks Different Levels of Abstraction Feature Representation 25

26. Hierarchical Learning • Natural progression from low level to high level structure as seen in natural complexity Different Levels of Abstraction Feature Representation 2626 • Easier to monitor what is being learnt and to guide the machine to better subspaces • A good lower level representation can be used for many distinct tasks

27. Classic Deep Architecture Input layer Hidden layers Output layer 27

28. Modern Deep Architecture Input layer Hidden layers Output layer movie time: http://www.cs.toronto.edu/~hinton/adi/index.htm 28

29. [Kudos to Richard Socher, for this eloquent summary :) ] • Manually designed features are often over-specified, incomplete and take a long time to design and validate • Learned Features are easy to adapt, fast to learn • Deep learning provides a very flexible, (almost?) universal, learnable framework for representing world, visual and linguistic information. • Deep learning can learn unsupervised (from raw text/audio/ images/whatever content) and supervised (with specific labels like positive/negative) Why Deep Learning ? 29

30. Word Embeddings 30

31. 31 What about NLP ? 1. Language is ambiguous:  Every sentence has many possible interpretations. 2. Language is productive:  We will always encounter new words or new constructions 3. Language is culturally speciﬁc Some of the challenges in Language Understanding:

32. • NLP treats words mainly (rule-based/statistical approaches at least) as atomic symbols:  • or in vector space:  • also known as “one hot” representation. • Its problem ? Language Representation Love Candy Store [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 …] Candy [0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 …] AND Store [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 …] = 0 ! 32

33. Language Representation 33 - Johan Boye, lecture 2 Term-document matrix = Sparse!

34. Distributional representations “You shall know a word by the company it keeps”  (J. R. Firth 1957) One of the most successful ideas of modern statistical NLP! these words represent banking • Hard (class based) clustering models • Soft clustering models 34

35. Distributional hypothesis He ﬁlled the wampimuk, passed it around and we all drunk some We found a little, hairy wampimuk sleeping behind the tree (McDonald & Ramscar 2001) 35

36. Distributional semantics Landauer and Dumais (1997), Turney and Pantel (2010), … 36

37. Distributional semantics Distributional meaning as co-occurrence vector: 37

38. Distributional representations • Taking it further: • Continuous word embeddings • Combine vector space semantics with the prediction of probabilistic models • Words are represented as a dense vector: Candy = 38

39. Word Embeddings: SocherVector Space Model adapted rom Bengio, “Representation Learning and Deep Learning”, July, 2012, UCLA In a perfect world: 39

40. Word Embeddings: SocherVector Space Model adapted rom Bengio, “Representation Learning and Deep Learning”, July, 2012, UCLA In a perfect world: the country of my birth the place where I was born 40

41. • Can theoretically (given enough units) approximate “any” function • and fit to “any” kind of data • Efficient for NLP: hidden layers can be used as word lookup tables • Dense distributed word vectors + efficient NN training algorithms: • Can scale to billions of words ! Why Neural Networks for NLP? 41

42. Word Embeddings: SocherVector Space Model Figure (edited) from Bengio, “Representation Learning and Deep Learning”, July, 2012, UCLA In a perfect world: the country of my birth the place where I was born ? … 42

43. Compositionality Principle of compositionality: the “meaning (vector) of a complex expression (sentence) is determined by: — Gottlob Frege   (1848 - 1925) - the meanings of its constituent expressions (words) and - the rules (grammar) used to combine them” 43

44. • How do we handle the compositionality of language in our models? 44 Compositionality

45. • How do we handle the compositionality of language in our models? • Recursion :  the same operator (same parameters) is applied repeatedly on diﬀerent components 45 Compositionality

46. • How do we handle the compositionality of language in our models? • Option 1: Recurrent Neural Networks (RNN) 46 RNN 1: Recurrent Neural Networks (we ignore recurrent NN’s for this talk)

47. • How do we handle the compositionality of language in our models? • Option 2: Recursive Neural Networks (also sometimes called RNN) 47 RNN 2: Recursive Neural Networks

48. Recursive Neural Tensor Network 48

49. Recursive Neural Tensor Network 49 code & info: http://www.socher.org/index.php/Main/ ParsingNaturalScenesAndNaturalLanguageWithRecursiveNeuralNetworks Socher, R., Liu, C.C., NG, A.Y., Manning, C.D. (2011)   Parsing Natural Scenes and Natural Language with Recursive Neural Networks

50. NP PP/IN NP DT NN PRP$ NN Parse Tree Recurrent NN for Vector Space 50

51. NP PP/IN NP DT NN PRP$ NN Parse Tree INDT NN PRP NN Compositionality 51 Recurrent NN: CompositionalityRecurrent NN for Vector Space

52. NP IN NP PRP NN Parse Tree DT NN Compositionality 52 Recurrent NN: CompositionalityRecurrent NN for Vector Space

53. NP IN NP DT NN PRP NN PP NP (S / ROOT) “rules” “meanings” Compositionality 53 Recurrent NN: CompositionalityRecurrent NN for Vector Space

54. Vector Space + Word Embeddings: Socher 54 Recurrent NN: CompositionalityRecurrent NN for Vector Space

55. Vector Space + Word Embeddings: Socher 55 Recurrent NN for Vector Space

56. Word Embeddings: Turian (2010) Turian, J., Ratinov, L., Bengio, Y. (2010). Word representations: A simple and general method for semi-supervised learning code & info: http://metaoptimize.com/projects/wordreprs/56

57. Word Embeddings: Turian (2010) Turian, J., Ratinov, L., Bengio, Y. (2010). Word representations: A simple and general method for semi-supervised learning code & info: http://metaoptimize.com/projects/wordreprs/ 57

58. Word Embeddings: Demo

59. Word Embeddings: Collobert & Weston (2011) Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P. (2011) . Natural Language Processing (almost) from Scratch 59

60. Polysemous-embeddings: Stanford (2012) Eric H. Huang, Richard Socher, Christopher D. Manning, Andrew Y. Ng (2012)  Improving Word Representations via Global Context and Multiple Word Prototypes 60

61. Linguistic Regularities: Mikolov (2013) code & info: https://code.google.com/p/word2vec/ Mikolov, T., Yih, W., & Zweig, G. (2013). Linguistic Regularities in Continuous Space Word Representations 61

62. Word Embeddings for MT: Mikolov (2013) Mikolov, T., Le, V. L., Sutskever, I. (2013) .   Exploiting Similarities among Languages for Machine Translation 62

63. Word Embeddings for MT: Kiros (2014) Kiros, R., Zemel, R. S., Salakhutdinov, R. (2014) .   A Multiplicative Model for Learning Distributed Text-Based Attribute Representations 63

64. Recursive Deep Models & Sentiment: Socher (2013) Socher, R., Perelygin, A., Wu, J., Chuang, J.,Manning, C., Ng, A., Potts, C. (2013)   Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. code & demo: http://nlp.stanford.edu/sentiment/index.html 64

65. Paragraph Vectors: Le & Mikolov (2014) Le, Q., Mikolov,. T. (2014) Distributed Representations of Sentences and Documents 65 • add context (sentence, paragraph, document) to word vectors during training ! Results on Stanford Sentiment   Treebank dataset:

66. Paragraph Vectors: Dai et al. (2014) Dai, A., Olah,. C., Le, Q., Corrado, G. (2014) Document Embedding with Paragraph Vectors 66

67. Paragraph Vectors: Dai et al. (2014) Dai, A., Olah,. C., Le, Q., Corrado, G. (2014) Document Embedding with Paragraph Vectors 67

68. Paragraph Vectors: Dai et al. (2014) Dai, A., Olah,. C., Le, Q., Corrado, G. (2014) Document Embedding with Paragraph Vectors 68 Nearest neighbours to the machine learning paper “Distributed Representations of Sentences and Documents” in arXiv.

69. Joint Image-Word Embeddings 69

70. 1. Multimodal representation learning 2. Generating descriptions of images 3. Ranking images and captions (“image-sentence ranking”) Some Current Approaches 70

71. Bags of Visual Words 71 Source credit : K. Grauman, B. Leibe

72. Bags of Visual Words (Sivic & Zisserman 2003) standard BoW issues however What we get: But we want: • visual word order/relations • location • scale/viewpoint invariance • … 72

73. Zero-shot Learning • skip-gram text model on wikipedia corpus of 5.7 million documents (5.4 billion words) - approach from (Mikolov et al. ICLR 2013) 73 Frome, A., Corrado, G.S., Shlens, J., Bengio, S., Dean, J., Mikolov, T., Ranzato, M.A. (2013)   Devise: A deep visual-semantic embedding model DeViSE model

74. Encoder: A deep convolutional network (CNN) and long short- term memory recurrent network (LSTM) for learning a joint image-sentence embedding. Decoder: A new neural language model that combines structure and content vectors for generating words one at a time in sequence. Encoder-Decoder pipeline 74 Kiros, R., Salakhutdinov, R., Zemerl, R. S. (2014)   Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models (Kiros et al 2014)

75. • captures Multimodal linguistic regularities Encoder-Decoder pipeline 75

76. • captures Multimodal linguistic regularities Encoder-Decoder pipeline 76 (PCA projection of (300-dimensional) word and image representations)

77. 77 Vinyals, O., Toshev, A., Bengio, S., Erhan. D. (2015)   Show and Tell: A Neural Image Caption Generator Joint Visual-Semantic embedding Karpathy, A., Fei Fei, L. (2015)   Deep Visual-Semantic Alignments for Generating Image Descriptions CNN+LSTM CNN+RNN

78. 78 Karpathy, A., Fei Fei, L. (2015)   Deep Visual-Semantic Alignments for Generating Image Descriptions Joint Visual-Semantic embedding

79. 79 Joint Visual-Semantic embedding Karpathy, A., Fei Fei, L. (2015)   Deep Visual-Semantic Alignments for Generating Image Descriptions

80. 80 Joint Visual-Semantic embedding Karpathy, A., Fei Fei, L. (2015)   Deep Visual-Semantic Alignments for Generating Image Descriptions

81. Joint Visual-Semantic embedding 81 Karpathy, A., Fei Fei, L. (2015)   Deep Visual-Semantic Alignments for Generating Image Descriptions demo

82. Any Questions?

83. Download example code samples from https://github.com/graphiﬁc/DL-Meetup-intro 83 git clone --recursive https://github.com/graphific/ DL-Meetup-intro.git Wanna Play ? Code! (more at http://deeplearning.net/ )

84. • Theano - CPU/GPU symbolic expression compiler in python (from LISA lab at University of Montreal). http://deeplearning.net/software/theano/ • Pylearn2 - library designed to make machine learning research easy. http://deeplearning.net/software/ pylearn2/ • Torch - Matlab-like environment for state-of-the-art machine learning algorithms in lua (from Ronan Collobert, Clement Farabet and Koray Kavukcuoglu) http://torch.ch/ • more info: http://deeplearning.net/software links/ Wanna Play ? Wanna Play ? General Deep Learning 84

85. • RNNLM (Mikolov)  http://rnnlm.org • NB-SVM  https://github.com/mesnilgr/nbsvm • Word2Vec (skipgrams/cbow)  https://code.google.com/p/word2vec/ (original)  http://radimrehurek.com/gensim/models/word2vec.html (python) • GloVe  http://nlp.stanford.edu/projects/glove/ (original)  https://github.com/maciejkula/glove-python (python) • Socher et al / Stanford RNN Sentiment code:  http://nlp.stanford.edu/sentiment/code.html • Deep Learning without Magic Tutorial:  http://nlp.stanford.edu/courses/NAACL2013/ Wanna Play ? NLP 85

86. • cuda-convnet2 (Alex Krizhevsky, Toronto) (c++/ CUDA, optimized for GTX 580)   https://code.google.com/p/cuda-convnet2/ • Caﬀe (Berkeley) (Cuda/OpenCL, Theano, Python)  http://caﬀe.berkeleyvision.org/ • OverFeat (NYU)   http://cilvr.nyu.edu/doku.php?id=code:start Wanna Play ? Computer Vision 86

87. 87

88. Impact on Computer Vision 88

89. Impact on Computer Vision (from Clarifai)89

90. Impact on Audio Processing Speech Recognition 90

91. Impact on Audio Processing TIMIT Speech Recognition (from: Clarifai)91

92. C&W 2011 Impact on Natural Language Processing Pos: Toutanova et al.  2003) Ner: Ando & Zhang   2005 C&W 2011 92

93. Impact on Natural Language Processing Named Entity Recognition: 93

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