Deep Learning, an interactive introduction for NLP-ers
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@graphific Roelof Pieters Introduc0on to Deep Learning for NLP 22 January 2015 Stockholm Natural Language Processing Meetup FEEDA Slides at: http://www.slideshare.net/roelofp/220115dlmeetup 1
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Transcript of Deep Learning, an interactive introduction for NLP-ers
@graphificRoelof Pieters
Introduc0on to Deep Learning for NLP
22 January 2015 Stockholm Natural Language Processing Meetup
FEEDA
Slides at:http://www.slideshare.net/roelofp/220115dlmeetup
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Deep Learning ???
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A couple of headlines… [all November ’14]
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(source: Google Trends)4
Machine Learning ??
- Audience Check -
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• “Brain” inspired / simulations:
• vision: make learning algorithms better and easier to use
• goal: revolutions in (practical) advances for machine learning and AI
• Deep Learning = subfield of Machine Learning
Deep Learning ??
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Biological Inspiration
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Deep Learning ??
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DL: Impact
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Speech Recognition
DL: Impact
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Deep Learning for the win!a few examples:
• IJCNN 2011 Traffic Sign Recognition Competition• ISBI 2012 Segmentation of neuronal structures in EM stacks
challenge• ICDAR 2011 Chinese handwriting recognition
• Deals with “construction and study of systems that can learn from data”
Machine Learning ??
A computer program is said to learn from experience (E) with respect to some class of tasks (T) and performance measure (P), if its performance at tasks in T, as measured by P, improves with experience E
— T. Mitchell 1997
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Machine Learning ??
Traditional Programming:
Data
ProgramOutput
DataProgram
Output
Machine Learning:
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Supervised (inductive) learning
• Training data includes desired outputs
Unsupervised learning
• Training data does not include desired outputs
Semi-supervised learning
• Training data includes a few desired outputs
Reinforcement learning
• Rewards from sequence of actions
Types of Learning
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ML: Traditional Approach
1. Gather as much LABELED data as you can get
2. Throw some algorithms at it (mainly put in an SVM and keep it at that)
3. If you actually have tried more algos: Pick the best
4. Spend hours hand engineering some features / feature selection / dimensionality reduction (PCA, SVD, etc)
5. Repeat…
For each new problem/question::
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Machine Learning for NLP
Data
Classic Approach: Data is fed into a learning algorithm:
Learning Algorithm
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Machine Learning for NLP
some of the (many) treebank datasets
source: http://www-nlp.stanford.edu/links/statnlp.html#Treebanks
!
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Penn TreebankThat’s a lot of “manual” work:
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• the students went to class
DT NN VB P NN
• plays well with others
VB ADV P NN
NN NN P DT
• fruit flies like a banana
NN NN VB DT NN
NN VB P DT NN
NN NN P DT NN
NN VB VB DT NN
With a lot of issues:
Penn Treebank
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Machine Learning for NLP
Learning AlgorithmData
“Features”
PredictionPrediction/Classifier
train set
test set
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Machine Learning for NLP
Learning Algorithm
“Features”
PredictionPrediction/Classifier
train set
test set
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Machine Learning for NLP
• Until the early 1990’s, NLP systems were built manually with hand-crafted dictionaries and rules.
• As large electronic text corpora became increasingly available, researchers began using machine learning techniques to automatically build NLP systems.
• Today, the vast majority of NLP systems use machine learning.
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2. Neural Networks and a short history lesson
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Perceptron (1957)
Frank Rosenblatt (1928-1971)
Original Perceptron
Simplified model:
(From Perceptrons by M. L Minsky and S. Papert, 1969, Cambridge, MA: MIT Press. Copyright 1969
by MIT Press.
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Perceptron (1957)
Perceptron Research, youtube clip: https://www.youtube.com/watch?v=cNxadbrN_aI&feature=youtu.be&t=12
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Perceptron (1957)
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or
Multilayer Perceptron (1986)
inputs
weights
biasactivation
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Neuron Model
All you need to know:
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Activation functions
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Backpropagation (1974/1986)
1974 Paul Werbos’ invents Backpropagation algorithm for NN1986 Backdrop popularized by Rumelhart, Hinton, Williams1990: Renewed Interest in NN’s
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Backprop Renaissance
Forward Propagation
• Sum inputs, produce activation, feed-forward
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Backprop Renaissance
Back Propagation (of error)
• Calculate total error at the top
• Calculate contributions to error at each step going backwards
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• Compute gradient of example-wise loss wrt parameters
• Simply applying the derivative chain rule wisely
• If computing the loss (example, parameters) is O(n)computation, then so is computing the gradient
Backpropagation
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Simple Chain Rule
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Training procedure
• Initialize randomly• Sequentially give it data.• See what the difference is between network output
and actual output.• Update the weights according to this error.• End result: give a model input, and it produces a
proper output.
Quest for the weights. The weights are the model!
To reiterate:
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So why only now?
• Inspired by the architectural depth of the brain, researchers wanted for decades to train deep multi-layer neural networks.
• No successful attempts were reported before 2006 …Exception: convolutional neural networks, LeCun 1998
• SVM: Vapnik and his co-workers developed the Support Vector Machine (1993) (shallow architecture).
• Breakthrough in 2006!
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2006 Breakthrough
• More data
• Faster hardware: GPU’s, multi-core CPU’s
• Working ideas on how to train deep architectures
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2006 Breakthrough
• More data
• Faster hardware: GPU’s, multi-core CPU’s
• Working ideas on how to train deep architectures
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2006 Breakthrough
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2006 Breakthrough
• More data
• Faster hardware: GPU’s, multi-core CPU’s
• Working ideas on how to train deep architectures
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2006 Breakthrough
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2006 Breakthrough
• More data
• Faster hardware: GPU’s, multi-core CPU’s
• Working ideas on how to train deep architectures
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2006 Breakthrough
Stacked Restricted Boltzman Machines* (RBM) Hinton, G. E, Osindero, S., and Teh, Y. W. (2006).A fast learning algorithm for deep belief nets.Neural Computation, 18:1527-1554.
Stacked Autoencoders (AE) Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H. (2007).Greedy Layer-Wise Training of Deep Networks,Advances in Neural Information Processing Systems 19
* called Deep Belief Networks (DBN)42
3. Deep Learning onwards we go…
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Hierarchies
Efficient
Generalization
Distributed
Sharing
Unsupervised*
Black Box
Training Time
Major PWNAGE!
Much Data
Why go Deep ?
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No More Handcrafted Features !
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— 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?
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Biological JustificationDeep Learning = Brain “inspired”Audio/Visual Cortex has multiple stages == Hierarchical
• Computational Biology • CVAP
• Jorge Dávila-Chacón • “that guy”
“Brainiacs” “Pragmatists”vs
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Different Levels of Abstraction
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Hierarchical Learning
• Natural progression from low level to high level structure as seen in natural complexity
Different Levels of AbstractionFeature Representation
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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 AbstractionFeature Representation
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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 AbstractionFeature Representation
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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 AbstractionFeature Representation
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• Shared Low Level Representations
• Multi-Task Learning
• Unsupervised Training
Generalizable Learning
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• Shared Low Level Representations
• Multi-Task Learning
• Unsupervised Training
• Partial Feature Sharing
• Mixed Mode Learning
• Composition of Functions
Generalizable Learning
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Classic Deep Architecture
Input layer
Hidden layers
Output layer
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Modern Deep Architecture
Input layer
Hidden layers
Output layer
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Deep Learning: Why? (again)
Beat state of the art in many areas:• Language Modeling (2012, Mikolov et al)• Image Recognition (Krizhevsky won
2012 ImageNet competition)• Sentiment Classification (2011, Socher et
al)• Speech Recognition (2010, Dahl et al)• MNIST hand-written digit recognition (Ciresan et al, 2010)
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One Model rules them all ?DL approaches have been successfully applied to:
Deep Learning: Why for NLP ?
Automatic summarization Coreference resolution Discourse analysis
Machine translation Morphological segmentation Named entity recognition (NER)
Natural language generation
Natural language understanding
Optical character recognition (OCR)
Part-of-speech tagging
Parsing
Question answering
Relationship extraction
sentence boundary disambiguation
Sentiment analysis
Speech recognition
Speech segmentation
Topic segmentation and recognition
Word segmentation
Word sense disambiguation
Information retrieval (IR)
Information extraction (IE)
Speech processing
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- COFFEE BREAK -after the break we return with: CODE
Download the code samples already now from:https://github.com/graphific/DL-Meetup-intro
http://goo.gl/abX1E2 shortened url: 60
• Deep Neural Network
• Multilayer Perceptron (MLP) or Artificial Neural Network (ANN)
1. MLP
Logistic regression
Training regime: Stochastic Gradient Descent (SGD) with minibatches
MNIST dataset
Simple hidden layer
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2. Convolutional Neural Network
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from: Krizhevsky, Sutskever, Hinton. (2012). ImageNet Classification with Deep Convolutional Neural Networks[breakthrough in object recognition, Imagenet 2012]
Convolutional Neural Network
http://ufldl.stanford.edu/wiki/index.php/Feature_extraction_using_convolution
movie time:http://www.cs.toronto.edu/~hinton/adi/index.htm
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Thats it, no more code! (for now)
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Deep Learning: Future Developments
Currently an explosion of developments• Hessian-Free networks (2010)• Long Short Term Memory (2011)• Large Convolutional nets, max-pooling (2011)• Nesterov’s Gradient Descent (2013)
Currently state of the art but...• No way of doing logical inference (extrapolation)• No easy integration of abstract knowledge• Hypothetic space bias might not conform with reality
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Deep Learning: Future Challenges
a
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Szegedy, C., Wojciech, Z., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R. (2013) Intriguing properties of neural networks
L: correctly identified, Center: added noise x10, R: “Ostrich”
• 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 ?
• 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 ?
as PhD candidate KTH/CSC:“Always interested in discussing
Machine Learning, Deep Architectures, Graphs, and
Language Technology”
In touch!
[email protected]/~roelof/
Internship / EntrepeneurshipAcademic/Researchas CIO/CTO Feeda:
“Always looking for additions to our brand new R&D team”
[Internships upcoming on KTH exjobb website…]
Feeda
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Were Hiring!
Feeda
• Dev Ops • Software Developers • Data Scientists
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Thanks for listening
Mingling time!
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Can’t get enough? Come to my talk Tomorrow (friday)
Description on KTH website
Visual-Semantic Embeddings: some thoughts on Language
Roelof Pieters TCS/CSCFriday jan 23 13:30.
Room 304, Teknikringen 14 level 3
Appendum
Some of the exciting recent developments in NLPespecially Distributed Semantics
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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/ 74
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/ 75
Word Embeddings: Collobert & Weston (2011)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P. (2011) . Natural Language Processing (almost) from Scratch
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Multi-embeddings: Stanford (2012)
Eric H. Huang, Richard Socher, Christopher D. Manning, Andrew Y. Ng Improving Word Representations via Global Context and Multiple Word Prototypes
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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
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Word Embeddings for MT: Mikolov (2013)
Mikolov, T., Le, V. L., Sutskever, I. (2013) . Exploiting Similarities among Languages for Machine Translation79
Recursive Deep Models & Sentiment: Socher (2013)
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Chris Manning, Andrew Ng and Chris Potts. 2013. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. EMNLP 2013
code & demo: http://nlp.stanford.edu/sentiment/index.html80
Paragraph Vectors: Le & Mikolov (2014)
Le, Q., Mikolov,. T. (2014) Distributed Representations of Sentences and Documents
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• add context (sentence, paragraph, document) to word vectors during training
!
Results on Stanford Sentiment Treebank dataset:
Global Vectors, GloVe: Stanford (2014)
Pennington, P., Socher, R., Manning,. D.M. (2014). GloVe: Global Vectors for Word Representation
code & demo: http://nlp.stanford.edu/projects/glove/
vsresults on the word analogy task
“similar accuracy”
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Dependency-based Embeddings: Levy & Goldberg (2014)
Levy, O., Goldberg, Y. (2014). Dependency-Based Word Embeddings
code & demo: https://levyomer.wordpress.com/2014/04/25/dependency-based-word-embeddings/
- Syntactic Dependency Context
Australian scientist discovers star with telescope
- Bag of Words (BoW) Context
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Precision
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Recall$
“Dependency-based embeddings have more
functional similarities”
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