NLP and Deep Learning for non_experts
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Natural Language processing basics. Deep Learning, feed forward neural network, convolutional neural network, text classification
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NLP and Deep Learning for non_experts
- 1. NLP & Deep Learning for non-experts Sanghamitra Deb Staff Data Scientist Chegg Inc
- 2. How to start projects in machine learning? • Kaggle competitions --- • Make sure to solve the ML problems for concept development before competing
- 3. How to start projects in machine learning? • Kaggle competitions --- • Make sure to solve the ML problems for concept development before competing
- 4. How to start projects in machine learning? • Self guided workshops/projects --- lets say you have data from Zomato • Restaurant recommendation -- user based, content similarity based. • Restaurant tags from reviews. • Sentiment analysis from reviews.
- 5. Outline • What is NLP • Bag of Words model for sentiment analysis using scikit learn • DeepDive into deep learning • Solve the sentiment analysis problem using keras • A short into Convolution Neural Networks (CNN)
- 6. What is Natural Language Processing? • Giving structure to unstructured data • Learn properties of the data that makes decision making simple • Provide concise information to drive intelligence of different systems.
- 7. Why? • Unstructured data cannot be consumed directly • Automate simple and complex functionalities • Inferences from text data becomes queriable. This could help with regular BU reports • Understand customers better and take necessary actions for better experience.
- 8. Applications • Categorization of text • Building domain specific Knowledge Graph • Recommendations • Web --- Search • HR --- people analytics • Medical --- drug discovery, automated diagnosis • ………..
- 9. What are the underlying tasks? • Syntactic Parsing of sentences --- parsing based on structure • Part of Speech Tagging • Semantic Parsing -- mapping text directly into formal query language, e.g. SQL queries for a pre-determined database schema. • Dialogue state tracking --- chatbots • Machine Translation • Language modeling • Text extraction • Classification
- 10. Text Classification Text Pre - processing Collecting Training Data Model Building Offline SME • Reduces noise • Ensures quality • Improves overall performance • Training Data Collection / Examples of classes that we are trying to model • Model performance is directly correlated with quality of training data • Model selection • Architecture • Parameter Tuning User Online Model Evaluation
- 11. Text Data Data Source -- https://archive.ics.uci.edu/ml/datasets/Sentiment+Labelled+Sentences
- 12. Model Building: a simple Bag of words (BOW) model https://realpython.com/python-keras-text-classification/
- 13. Model Building: a simple BOW model https://realpython.com/python-keras-text-classification/
- 14. Deep Learning Deep learning algorithms seek to exploit the unknown structure in the input distribution in order to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features. --- Yoshua Bengio a kind of learning where the representation you form have several levels of abstraction, rather than a direct input to output --- Peter Norvig When you hear the term deep learning, just think of a large deep neural net. Deep refers to the number of layers typically and so this kind of the popular term that’s been adopted in the press. I think of them as deep neural networks generally. --- Andrew Ng
- 15. Why now? • Explosion in labelled data. • Exponential growth in computation power with cloud computing and availability of GPUs • Improvements in setting initial conditions and activation functions
- 16. Neural Network Simulate the brain and get neurons densely interconnected in a computer such that it can learn things, recognize patterns and take decisions?
- 17. Neural Network Simulate the brain and get neurons densely interconnected in a computer such that it can learn things, recognize patterns and take decisions? What is a neuron?
- 18. Neural Network Simulate the brain and get neurons densely interconnected in a computer such that it can learn things, recognize patterns and take decisions? What is a neuron?
- 22. Neural Network • Each node is a function with input and output vectors • Every network structure is defined by a set of functions
- 23. Output Layer
- 24. • Loss is minimized using Gradient Descent • Find network parameters such that the loss is minimized • This is done by taking derivatives of the loss wrt parameters. • Next the parameters are updated by subtracting learning rate times the derivative
- 25. Commonly used loss functions • Mean Squared Error Loss • Mean Squared Logarithmic Error Loss • Mean Absolute Error Loss Regression Loss Functions • Binary Cross-Entropy • Hinge Loss • Squared Hinge Loss Binary Classification Loss Functions • Multi-Class Cross-Entropy Loss • Sparse Multiclass Cross-Entropy Loss • Kullback Leibler Divergence Loss Multi-Class Classification Loss Functions
- 27. Dropout -- avoid overfitting • Large weights in a neural network are a sign of a more complex network that has overfit the training data. • Probabilistically dropping out nodes in the network is a simple and effective regularization method. • A large network with more training and the use of a weight constraint are suggested when using dropout.
- 29. Adam Optimization • adaptive moment estimation • The method computes individual adaptive learning rates for different parameters from estimates of first and second moments of the gradients. • Calculates an exponential moving average of the gradient and the squared gradient, parameters control the decay rates of these moving averages. https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/
- 30. Activation Functions • Sigmoid/ Softmax • Tanh • Relu • Leaky Relu • Swish
- 31. Activation Functions • Sigmoid/ Softmax • Tanh • Relu • Leaky Relu • Swish
- 32. Activation Functions • Sigmoid/ Softmax • Tanh • Relu • Leaky Relu • Swish Derivative
- 33. Activation Functions • Sigmoid/ Softmax • Tanh • Relu • Leaky Relu • Swish Derivative a = max(0,z)
- 34. Activation Functions • Sigmoid/ Softmax • Tanh • Relu • Leaky Relu • Swish
- 35. Activation Functions • Sigmoid/ Softmax • Tanh • Relu • Leaky Relu • Swish https://arxiv.org/abs/1710.05941v1
- 37. Text Classification using feed forward NN https://realpython.com/python-keras-text-classification/
- 38. Text Classification using feed forward NN
- 39. Fit & measure accuracy! plot_history(history) Clearly overfits the data!
- 40. Can we do better? Word Embeddings • Words are represented as dense vectors • These vectors are • Learned during the training task by the neural network • Pre-trained, learned from Language Models • Encode the semantic meaning of the word.
- 41. Text Pre-processing with Keras PaddingTokenizing
- 42. Start with an Embedding Layer • Embedding Layer of Keras which takes the previously calculated integers and maps them to a dense vector of the embedding. o Parameters Ø input_dim: the size of the vocabulary Ø output_dim: the size of the dense vector Ø input_length: the length of the sequence Hope to see you soon Nice to see you again After training https://stats.stackexchange.com/questions/270546/how-does-keras-embedding-layer-work
- 43. Add a pooling layer • MaxPooling1D/AveragePooling1D or a GlobalMaxPooling1D/GlobalAveragePooling1D layer • way to downsample (a way to reduce the size of) the incoming feature vectors. • Global max/average pooling takes the maximum/average of all features whereas in the other case you have to define the pool size.
- 45. Training Using pre-trained word embeddings will lead to an accuracy of 0.82. This is a case of transfer learning. https://realpython.com/python-keras-text-classification
- 46. Embeddings + Maxpooling -- Benifits • Power of generalization --- embeddings are able to share information across similar features. • Fewer nodes with zero values.
- 47. Convolution Neural Network Detect features ! Downsample.
- 48. What is a CNN? In a traditional feedforward neural network we connect each input neuron to each output neuron in the next layer. That’s also called a fully connected layer, or affine layer. • We use convolutions over the input layer to compute the output. This results in local connections, where each region of the input is connected to a neuron in the output. Each layer applies different filters and combines the result • During the training phase, a CNN automatically learns the values of its filters based on the task you want to perform. Tricky --- dimensions keep changing as we go from one layer to another
- 49. Model definition Embedding_dim = 50 maxlen=10
- 50. Advantages of CNN • Character Based CNN • Has the ability to deal with out of vocabulary words. This makes it particularly suitable for user generated raw text. • Works for multiple languages. • Model size is small since the tokens are limited to the number of characters ~ 70. This makes real life deployments easier and faster. • Networks with convolutional and pooling layers are useful for classification tasks in which we expect to find strong local clues regarding class membership.
- 51. Takeaways! • If you have text data you need to use NLP • Try a simple bag of words model for your data • Having a high level understanding of deep learning will help with better judgement in architecture design and choice of parameters. • Deep Learning has the potential to give high performance, you do need large amount of training data for the benefits.
- 53. Visualization of the architecture 50 10 GlobalMaxPool1D DenseLayer Sigmoid Cov1D
- 55. Appendix
- 57. Character Based CNNs. https://papers.nips.cc/paper/5782-character-level-convolutional-networks-for-text-classification.pdf • Embedding Layer • Six convolutional layers, and 3 convolutional layers followed by a max pooling layer • Two fully connected layer(dense layer in keras), neuron units are 1024. • Output layer(dense layer), neuron units depends on classes. In this task, we set it 4.
- 58. Pre-processing