Deep Learning for NLP
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How to take your first steps in doing text generation with Deep Learning using an example case.
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Deep Learning for NLP
- 1. Deep Learning for Natural Language Processing Bargava Subramanian @bargava Amit Kapoor @amitkaps 1
- 3. Put these adjectives in order: [adj.] + [Knife] — old — French — lovely — green — rectangular — whittling — silver — little 3
- 4. Which order is correct? lovely old silver rectangular green little French whittling knife old lovely French rectangular green little whittling silver knife lovely little old rectangular green French silver whittling knife 4
- 5. Grammar has rules opinion - size - age - shape - colour - origin -material - purpose [Noun] The right version: lovely little old rectangular green French silver whittling knife 5
- 6. We speak the grammar, yet we don't know it 6
- 7. Natural Language Problems are hard 7
- 8. Natural Language Programming Problems — Summarization — Text Classification (e.g. spam) — Sentiment / Emotion Analysis — Topic Modelling — Recommendations — Text Evaluation (e.g. grading) 8
- 9. Plan for this Session — Moving beyond Statistical Learning — Take first steps in NLP with Deep Learning — Showcase an example — Practical challenges to overcome 9
- 10. NLP Learning Process ___ [1] Frame: Problem definition [2] Acquire: Text ingestion [3] Refine: Text wrangling [4] Transform: Feature creation [5] Explore: Feature selection [6] Model: Model selection [7] Insight: Solution communication 10
- 11. Simple Case Demonetisation in India 11
- 12. Demonetisation in India On Nov 8th, 2016, the National Government announced that existing INR 1000 and INR 500 notes are no longer legal. 12
- 13. 13
- 14. Reactions on Twitter People started tweeting with the tag: #demonetisation 14
- 15. [1] Frame Create a viral tweet on #demonetisation 15
- 16. Traditional way of framing 1. Someone has to write a tweet. 2. Run it on the classifier 3. If probability is high, post it. 4. Else, goto step 1 The prediction will be a probability of a new tweet to go viral or not? 16
- 17. Generating tweets — Can we learn from historical tweets algorithmically to generate a viral tweet? — Not possible to do using traditional methods 17
- 18. Revised framing for Text Generation Generate a tweet algorithmically, that is likely to go viral 18
- 19. [2] Acquire Get the raw tweets data 19
- 20. Get Tweets on #demonetisation Write your own twitter api client to get json file or use a python package like Tweepy, but need to manage rate limiting etc. We used tweezer - an open source project to get twitter data Raw dataset - 30,000+ tweets from past 1 week. 20
- 21. [3] Refine How to categorise a tweet as viral or not? 21
- 22. Simple Approach for Labelling IF retweets + favourites > = 100 THEN Label = viral ELSE Label = normal 22
- 23. Sanitizing Tweets — Stopword — Stemming — Remove urls — Remove 'RT' — Remove 'n' 23
- 24. [4] Transform Creating Features from Text 24
- 25. Traditional methods to covert text to numeric — TF-IDF: Measures importance of a word in a document relative to the corpus — Bag-of-Word: Count of occurrences of a word in a document — n-grams: Count of every 1-word, 2-word, etc combinations in a document — entity & POS tagging: Transform sentence to parts-of-speech, extract entities and encode 25
- 26. Challenges in traditional methods of encoding — Sparse inputs — Input data space explodes — Context lost in encoding A quiet crowd entered the historic church != A historic crowd entered the quiet church 26
- 27. Deep Learning Approach Low-dimensional dense vectors for representation. — Tokenise characters (Faster) — Tokenise words (More accurate, but needs more memory) 27
- 28. Word Embedding — Learn high-quality word vectors — Similar words needs to be close to each other — Words can have multiple degrees of similarity 28
- 29. Word Embedding using word2vec Combines two approaches — skip-gram: Predicting word given its context — continuous bag-of-words: Predicting context given a word 29
- 30. word2vec: Example vec[queen] − vec[king] = vec[woman] − vec[man] 1 1 https://www.tensorflow.org/versions/r0.12/tutorials/word2vec/index.html 30
- 32. Feature Selection — Manual process in Traditional Approach — Feature selection happens automatically in Deep Learning 32
- 34. Recurrent Neural Network (RNN) — Network with loops — Allows information to persist — Enables connecting previous information to present task — Context preserved I grew up in Brazil and I speak ______________. portuguese 34
- 35. Unrolling over Time ____ [1] Think sequences - in input & output - Recognize Image -> Explain in words - Sentence(s) -> Sentiment Analysis - English - Spanish Translation - Video - task classification 35
- 36. Unrolled RNN [2] Multiple copies of the same network [3] Each pass message to its successor 2 2 http://colah.github.io/posts/2015-08-Understanding-LSTMs/ 36
- 40. Deep Learning Challenges — Data Size: RNN doesn't generalize well on small datasets — Relevant Corpus: Required to create domain specific word embedding — Deeper Networks: Empirically deeper networks have better accuracy — Training Time: RNNs take a long time to learn. 40
- 41. Use case: Chat Bots — Bookings — Customer Support — Help Desk Automation — ... 41
- 42. Tools to get started: Software Python Stack - Use spacy for NLP preprocessing - Use gensim for word2vec training - Start with keras - Have tensorflow as backend Use pre-trained models like word2vec for word embedding and similarly for RNNs 42
- 43. Tools to get started: Hardware Work on GPUs - Nvidia TitanX (suitable for consumers) - Tesla K80 (suitable for professionals) For detailed hardware choices: http://timdettmers.com/2015/03/09/deep- learning-hardware-guide/ 43
- 45. Reference: Deep Learning for NLP Notebooks and Material @ https://github.com/rouseguy/ DeepLearningNLP_Py - What is deep learning? - Motivation: Some use cases - Building blocks of Neural Networks (Neuron, Activation Function) - Backpropagation Algorithm - Word Embedding - word2vec - Introduction to keras - Multi-layer perceptron - Convolutional Neural Network - Recurrent Neural Network - Challenges in Deep Learning 45