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Shallow Processing: Summary

Shallow Processing: Summary. Shallow Processing Techniques for NLP Ling570 December 7, 2011. Roadmap. Looking back: Course units Tools and Techniques Looking forward Upcoming courses. Units #0,#1. Unit #0 (0.5 weeks): HW #1 Introduction to NLP & shallow processing Tokenization.

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Shallow Processing: Summary

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  1. Shallow Processing:Summary Shallow Processing Techniques for NLP Ling570 December 7, 2011

  2. Roadmap • Looking back: • Course units • Tools and Techniques • Looking forward • Upcoming courses

  3. Units #0,#1 • Unit #0 (0.5 weeks): • HW #1 • Introduction to NLP & shallow processing • Tokenization

  4. Units #0,#1 • Unit #0 (0.5 weeks): • HW #1 • Introduction to NLP & shallow processing • Tokenization • Unit #1 : • HW #2, #3 • Formal Languages and Automata (2.25 weeks) • Formal languages • Finite-state Automata • Finite-state Transducers • Morphological analysis

  5. Unit #2 • Unit #2 (3.25 weeks): • HW #4, #5, #6, #7 • NgramLanguage Models and HMMs • NgramLanguage Models and Smoothing • Part-of-speech (POS) tagging: • Ngram • Hidden Markov Models

  6. Units #3, #4 • Unit #3: Classification (3 weeks) • HW #8, #9 • Intro to classification & Mallets • POS tagging with classifiers • Chunking • Named Entity (NE) recognition

  7. Units #3, #4 • Unit #3: Classification (3 weeks) • HW #8, #9 • Intro to classification & Mallets • POS tagging with classifiers • Chunking • Named Entity (NE) recognition • Unit #4: Selected Topics (1.5 weeks) • HW #10 • Clustering • Information Extraction • Summary

  8. Techniques & Tools

  9. Main Techniques • Probability: • Chain rule:

  10. Main Techniques • Probability: • Chain rule: • Bayes’ Rule:

  11. Main Techniques • Formal languages: • Chomsky hierarchy

  12. Main Techniques • Formal languages: • Chomsky hierarchy • Regular languages, regular expressions, regular grammars, finite state automata

  13. Main Techniques • Formal languages: • Chomsky hierarchy • Regular languages, regular expressions, regular grammars, finite state automata • Regular relations, finite state transducers

  14. Main Techniques • Formal languages: • Chomsky hierarchy • Regular languages, regular expressions, regular grammars, finite state automata • Regular relations, finite state transducers • Finite state morphology: two-level morphological analysis

  15. Main Techniques • Formal languages: • Chomsky hierarchy • Regular languages, regular expressions, regular grammars, finite state automata • Regular relations, finite state transducers • Finite state morphology: two-level morphological analysis • Cascades of finite state transducers

  16. Main techniques • Language Modeling and Hidden Markov Models

  17. Main techniques • Language Modeling and Hidden Markov Models • N-gram language models • Maximum likelihood estimation

  18. Main techniques • Language Modeling and Hidden Markov Models • N-gram language models • Maximum likelihood estimation • Smoothing • Laplace, Good-Turing, Backoff, Interpolation

  19. Main techniques • Language Modeling and Hidden Markov Models • N-gram language models • Maximum likelihood estimation • Smoothing • Laplace, Good-Turing, Backoff, Interpolation • Hidden Markov Models • Markov assumptions • Forward algorithm & Viterbi decoding

  20. Main Techniques • Classification for NLP:

  21. Main Techniques • Classification for NLP: • Modeling NLP tasks as classification problems

  22. Main Techniques • Classification for NLP: • Modeling NLP tasks as classification problems • Developing feature representations for instances

  23. Main Techniques • Classification for NLP: • Modeling NLP tasks as classification problems • Developing feature representations for instances • Sequence labeling tasks and algorithms

  24. Main Techniques • Classification for NLP: • Modeling NLP tasks as classification problems • Developing feature representations for instances • Sequence labeling tasks and algorithms • Beam search

  25. Tools Developed • English tokenizer: HW#1 • FSA & FST acceptors: HW#2,#3 • FST morphological analyzer: HW#3 • N-gram language models with smoothing: HW#4,#5 • Authorship identification system: HW#5 • Hidden Markov Model: Training & Decoding: HW#6,7 • HMM POS Tagger • Classification-based text categorization, POS tagging • HW#8,#9 • Unsupervised POS tagger: HW#10

  26. Corpora & Systems • Data:

  27. Corpora & Systems • Data: • Penn Treebank • Wall Street Journal • Air Travel Information System (ATIS)

  28. Corpora & Systems • Data: • Penn Treebank • Wall Street Journal • Air Travel Information System (ATIS) • Project Gutenberg • Federalist Papers, Jane Austen novels • Systems:

  29. Corpora & Systems • Data: • Penn Treebank • Wall Street Journal • Air Travel Information System (ATIS) • Project Gutenberg • Federalist Papers, Jane Austen novels • Systems: • CARMEL Finite State Toolkit • Mallet Machine Learning Toolkit

  30. Looking Forward

  31. Winter Courses • Ling571: Deep Processing Techniques for NLP • Parsing, Semantics (Lambda Calculus), Generation

  32. Winter Courses • Ling571: Deep Processing Techniques for NLP • Parsing, Semantics (Lambda Calculus), Generation • Ling572: Advanced Statistical Methods in NLP • Roughly, machine learning for CompLing • Decision Trees, Naïve Bayes, MaxEnt, SVM, CRF,…

  33. Winter Courses • Ling571: Deep Processing Techniques for NLP • Parsing, Semantics (Lambda Calculus), Generation • Ling572: Advanced Statistical Methods in NLP • Roughly, machine learning for CompLing • Decision Trees, Naïve Bayes, MaxEnt, SVM, CRF,… • Ling567: Knowledge Engineering for Deep NLP • HPSG and MRS for novel languages

  34. Winter Courses • Ling571: Deep Processing Techniques for NLP • Parsing, Semantics (Lambda Calculus), Generation • Ling572: Advanced Statistical Methods in NLP • Roughly, machine learning for CompLing • Decision Trees, Naïve Bayes, MaxEnt, SVM, CRF,… • Ling567: Knowledge Engineering for Deep NLP • HPSG and MRS for novel languages • Ling575: Spoken Dialog Systems • Design, analysis, and implementation of SDS

  35. Tentative Outline for Ling572 • Unit #0 (0.5 weeks): Basics • Introduction • Feature representations • Classification review

  36. Tentative Outline for Ling572 • Unit #0 (0.5 weeks): Basics • Introduction • Feature representations • Classification review • Unit #1 (3 weeks): Classic Machine Learning • K Nearest Neighbors • Decision Trees • Naïve Bayes

  37. Tentative Outline for Ling572 • Unit #3: (4 weeks): Discriminative Classifiers • Feature Selection • Maximum Entropy Models • Support Vectors Machines

  38. Tentative Outline for Ling572 • Unit #3: (4 weeks): Discriminative Classifiers • Feature Selection • Maximum Entropy Models • Support Vectors Machines • Unit #4: (1.5 weeks): Sequence Learning • Conditional Random Fields • Transformation Based Learning

  39. Tentative Outline for Ling572 • Unit #3: (4 weeks): Discriminative Classifiers • Feature Selection • Maximum Entropy Models • Support Vectors Machines • Unit #4: (1.5 weeks): Sequence Learning • Conditional Random Fields • Transformation Based Learning • Unit #5: (1 week): Other Topics • Semi-supervised learning,…

  40. Ling572 Information • No required textbook: • Online readings and articles

  41. Ling572 Information • No required textbook: • Online readings and articles • More math/stat content than 570 • Probability, Information Theory, Optimization

  42. Ling572 Information • No required textbook: • Online readings and articles • More math/stat content than 570 • Probability, Information Theory, Optimization • Please try to register at least 2 weeks in advance

  43. Beyond Ling572 • Machine learning: • Graphical models • Bayesian approaches • Online learning • Reinforcement learning • ….

  44. Beyond Ling572 • Machine learning: • Graphical models • Bayesian approaches • Online learning • Reinforcement learning • …. • Applications: • Information Retrieval • Question Answering • Generation • Machine translation • ….

  45. Ling 575: Spoken Dialog Systems • Design, analysis, and implementation of SDS • Will be offered online • Please make sure you have a microphone • Arrive early to test • Loew 202: T: 3:30-5:50

  46. Notes • Grades should be submitted by 12/16 • Any issues (errors) with grades in Gradebook, please email by 12/14

  47. Notes • Grades should be submitted by 12/16 • Any issues (errors) with grades in Gradebook, please email by 12/14 • Graduation Planning: • Students in Group 1 (planning to graduate ~8/12) • Due dates in early-mid January • 1st thesis proposal draft • 1st pre-internship report

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