1. Introduction & Tokenization Ling570 Shallow Processing Techniques for NLP September 28, 2011

2. Roadmap Course Overview Tokenization Homework #1

3. Course Overview

4. Course Information Course web page: http://courses.washington.edu/ling570 Syllabus: Schedule and readings Links to other readings, slides, links to class recordings Slides posted before class, but may be revised Catalyst tools: GoPost discussion board for class issues CollectIt Dropbox for homework submission and TA comments Gradebook for viewing all grades

5. GoPost Discussion Board Main venue for course-related questions, discussion What not to post: Personal, confidential questions; Homework solutions What to post: Almost anything else course-related Can someone explain…? Is this really supposed to take this long to run? Key location for class participation Post questions or answers Your discussion space: Sanghoun & I will not jump in often

6. GoPost Emily’s 5-minute rule: If you’ve been stuck on a problem for more than 5 minutes, post to the GoPost! Mechanics: Please use your UW NetID as your user id Please post early and often ! Don’t wait until the last minute Notifications: Decide how you want to receive GoPost postings

7. Email Should be used only for personal or confidential issues Grading issues, extended absences, other problems General questions/comments go on GoPost Please send email from your UW account Include Ling570 in the subject If you don’t receive a reply in 24 hours, please follow-up

8. Homework Submission All homework should be submitted through CollectIt Tar cvf hw1.tar hw1_dir Homework due 11:45 Wednesdays Late homework receives 10%/day penalty (incremental) Most major programming languages accepted C/C++/C#, Java, Python, Perl, Ruby If you want to use something else, please check first Please follow naming, organization guidelines in HW Expect to spend 10-20 hours/week, including HW docs

9. Grading Assignments: 90% Class participation: 10% No midterm or final exams Grades in Catalyst Gradebook TA feedback returned through CollectIt Incomplete: only if all work completed up last two weeks UW policy

10. Recordings All classes will be recorded Links to recordings appear in syllabus Available to all students, DL and in class Please remind me to: Record the meeting (look for the red dot) Repeat in-class questions Note: Instructor’s screen is projected in class Assume that chat window is always public

11. Contact Info Gina: Email: levow@uw.edulevow@uw.edu Office hour: Fridays: 10-11 (before Treehouse meeting) Location: Padelford B-201 Or by arrangement Available by Skype or Adobe Connect All DL students should arrange a short online meeting TA: Sanghoun Song: Email: sanghoun@uw.edusanghoun@uw.edu Office hour: Time: TBD, see GoPost Location:

12. Online Option Please check you are registered for correct section CLMS online: Section A State-funded: Section B CLMS in-class: Section C NLT/SCE online (or in-class): Section D Online attendance for in-class students Not more than 3 times per term (e.g. missed bus, ice) Please enter meeting room 5-10 before start of class Try to stay online throughout class

13. Online Tip If you see: You are not logged into Connect. The problem is one of the following: the permissions on the resource you are trying to access are incorrectly set. Please contact your instructor/Meeting Host/etc. you do not have a Connect account but need to have one. For UWEO students: If you have just created your UW NetID or just enrolled in a course ….. Clear your cache, close and restart your browser

14. Course Description

15. Course Prerequisites Programming Languages: Java/C++/Python/Perl/.. Operating Systems: Basic Unix/linux CS 326 (Data structures) or equivalent Lists, trees, queues, stacks, hash tables, … Sorting, searching, dynamic programming,.. Automata, regular expressions,… Stat 391 (Probability and statistics): random variables, conditional probability, Bayes’ rule, ….

16. Textbook Jurafsky and Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2 nd edition, 2008 Available from UW Bookstore, Amazon, etc Reference: Manning and Schutze, Foundations of Statistical Natural Language Processing

17. Topics in Ling570 Unit #1: Formal Languages and Automata (2-3 weeks) Formal languages Finite-state Automata Finite-state Transducers Morphological analysis Unit #2: Ngram Language Models and HMMs Ngram Language Models and Smoothing Part-of-speech (POS) tagging: HMM Ngram

18. Topics in Ling570 Unit #3: Classification (2-3 weeks) Intro to classification POS tagging with classifiers Chunking Named Entity (NE) recognition Other topics (2 weeks) Intro, tokenization Clustering Information Extraction Summary

19. Roadmap Motivation: Applications Language and Thought Knowledge of Language Cross-cutting themes Ambiguity, Evaluation, & Multi-linguality Course Overview

20. Motivation: Applications Applications of Speech and Language Processing Call routing Information retrieval Question-answering Machine translation Dialog systems Spam tagging Spell-, Grammar- checking Sentiment Analysis Information extraction….

21. Shallow vs Deep Processing Shallow processing (Ling 570) Usually relies on surface forms (e.g., words) Less elaborate linguistic representations E.g. Part-of-speech tagging; Morphology; Chunking

22. Shallow vs Deep Processing Shallow processing (Ling 570) Usually relies on surface forms (e.g., words) Less elaborate linguistic representations E.g. Part-of-speech tagging; Morphology; Chunking Deep processing (Ling 571) Relies on more elaborate linguistic representations Deep syntactic analysis (Parsing) Rich spoken language understanding (NLU)

23. Shallow or Deep? Applications of Speech and Language Processing Call routing Information retrieval Question-answering Machine translation Dialog systems Spam tagging Spell-, Grammar- checking Sentiment Analysis Information extraction….

24. Building on Many Fields Linguistics: Morphology, phonology, syntax, semantics,.. Psychology: Reasoning, mental representations Formal logic Philosophy (of language) Theory of Computation: Automata,.. Artificial Intelligence: Search, Reasoning, Knowledge representation, Machine learning, Pattern matching Probability..

25. Language & Intelligence Turing Test: (1949) – Operationalize intelligence Two contestants: human, computer Judge: human Test: Interact via text questions Question: Can you tell which contestant is human? Crucially requires language use and understanding

26. Limitations of Turing Test ELIZA (Weizenbaum 1966) Simulates Rogerian therapist User: You are like my father in some ways ELIZA: WHAT RESEMBLANCE DO YOU SEE User: You are not very aggressive ELIZA: WHAT MAKES YOU THINK I AM NOT AGGRESSIVE…

27. Limitations of Turing Test ELIZA (Weizenbaum 1966) Simulates Rogerian therapist User: You are like my father in some ways ELIZA: WHAT RESEMBLANCE DO YOU SEE User: You are not very aggressive ELIZA: WHAT MAKES YOU THINK I AM NOT AGGRESSIVE... Passes the Turing Test!! (sort of)

28. Limitations of Turing Test ELIZA (Weizenbaum 1966) Simulates Rogerian therapist User: You are like my father in some ways ELIZA: WHAT RESEMBLANCE DO YOU SEE User: You are not very aggressive ELIZA: WHAT MAKES YOU THINK I AM NOT AGGRESSIVE... Passes the Turing Test!! (sort of) “You can fool some of the people....”

29. Limitations of Turing Test ELIZA (Weizenbaum 1966) Simulates Rogerian therapist User: You are like my father in some ways ELIZA: WHAT RESEMBLANCE DO YOU SEE User: You are not very aggressive ELIZA: WHAT MAKES YOU THINK I AM NOT AGGRESSIVE... Passes the Turing Test!! (sort of) “You can fool some of the people....” Simple pattern matching technique Very shallow processing

30. Turing Test Revived “On the web, no one knows you’re a….” Problem: ‘bots’ Automated agents swamp services Challenge: Prove you’re human Test: Something human can do, ‘bot can’t

31. Turing Test Revived “On the web, no one knows you’re a….” Problem: ‘bots’ Automated agents swamp services Challenge: Prove you’re human Test: Something human can do, ‘bot can’t Solution: CAPTCHAs Distorted images: trivial for human; hard for ‘bot

32. Turing Test Revived “On the web, no one knows you’re a….” Problem: ‘bots’ Automated agents swamp services Challenge: Prove you’re human Test: Something human can do, ‘bot can’t Solution: CAPTCHAs Distorted images: trivial for human; hard for ‘bot Key: Perception, not reasoning

33. Knowledge of Language What does HAL (of 2001, A Space Odyssey) need to know to converse? Dave: Open the pod bay doors, HAL. HAL: I'm sorry, Dave. I'm afraid I can't do that.

34. Knowledge of Language What does HAL (of 2001, A Space Odyssey) need to know to converse? Dave: Open the pod bay doors, HAL. HAL: I'm sorry, Dave. I'm afraid I can't do that. Phonetics & Phonology (Ling 450/550) Sounds of a language, acoustics Legal sound sequences in words

35. Knowledge of Language What does HAL (of 2001, A Space Odyssey) need to know to converse? Dave: Open the pod bay doors, HAL. HAL: I'm sorry, Dave. I'm afraid I can't do that. Morphology (Ling 570) Recognize, produce variation in word forms Singular vs. plural: Door + sg: -> door; Door + plural -> doors Verb inflection: Be + 1 st person, sg, present -> am

36. Knowledge of Language What does HAL (of 2001, A Space Odyssey) need to know to converse? Dave: Open the pod bay doors, HAL. HAL: I'm sorry, Dave. I'm afraid I can't do that. Part-of-speech tagging (Ling 570) Identify word use in sentence Bay (Noun) --- Not verb, adjective

37. Knowledge of Language What does HAL (of 2001, A Space Odyssey) need to know to converse? Dave: Open the pod bay doors, HAL. HAL: I'm sorry, Dave. I'm afraid I can't do that. Syntax (Ling 566: analysis; Ling 570 – chunking; Ling 571- parsing) Order and group words in sentence I’m I do, sorry that afraid Dave I can’t.

38. Knowledge of Language What does HAL (of 2001, A Space Odyssey) need to know to converse? Dave: Open the pod bay doors, HAL. HAL: I'm sorry, Dave. I'm afraid I can't do that. Semantics (Ling 571) Word meaning: individual (lexical), combined (compositional) ‘Open’ : AGENT cause THEME to become open; ‘pod bay doors’ : (pod bay) doors

39. Knowledge of Language What does HAL (of 2001, A Space Odyssey) need to know to converse? Dave: Open the pod bay doors, HAL. (request) HAL: I'm sorry, Dave. I'm afraid I can't do that. (statement) Pragmatics/Discourse/Dialogue (Ling 571, maybe) Interpret utterances in context Speech act (request, statement) Reference resolution: I = HAL; that = ‘open doors’ Politeness: I’m sorry, I’m afraid I can’t

40. Language Processing Pipeline Shallow Processing Deep Processing

41. Cross-cutting Themes Ambiguity How can we select among alternative analyses? Evaluation How well does this approach perform: On a standard data set? When incorporated into a full system? Multi-linguality Can we apply this approach to other languages? How much do we have to modify it to do so?

42. Ambiguity “I made her duck” Means....

43. Ambiguity “I made her duck” Means.... I caused her to duck down

44. Ambiguity “I made her duck” Means.... I caused her to duck down I made the (carved) duck she has

45. Ambiguity “I made her duck” Means.... I caused her to duck down I made the (carved) duck she has I cooked duck for her

46. Ambiguity “I made her duck” Means.... I caused her to duck down I made the (carved) duck she has I cooked duck for her I cooked the duck she owned

47. Ambiguity “I made her duck” Means.... I caused her to duck down I made the (carved) duck she has I cooked duck for her I cooked the duck she owned I magically turned her into a duck

48. Ambiguity: POS “I made her duck” Means.... I caused her to duck down I made the (carved) duck she has I cooked duck for her I cooked the duck she owned I magically turned her into a duck V N Pron Poss

49. Ambiguity: Syntax “I made her duck” Means.... I made the (carved) duck she has ((VP (V made) (NP (POSS her) (N duck)))

50. Ambiguity: Syntax “I made her duck” Means.... I made the (carved) duck she has ((VP (V made) (NP (POSS her) (N duck))) I cooked duck for her ((VP (V made) (NP (PRON her)) (NP (N (duck)))

51. Ambiguity Pervasive Pernicious Particularly challenging for computational systems Problem we will return to again and again in class

52. Tokenization Given input text, split into words or sentences Tokens: words, numbers, punctuation Example: Sherwood said reaction has been "very positive.” Sherwood said reaction has been ” very positive. " Why tokenize?

53. Tokenization Given input text, split into words or sentences Tokens: words, numbers, punctuation Example: Sherwood said reaction has been "very positive.” Sherwood said reaction has been ” very positive. " Why tokenize? Identify basic units for downstream processing

54. Tokenization Proposal 1: Split on white space Good enough

55. Tokenization Proposal 1: Split on white space Good enough? No Why not?

56. Tokenization Proposal 1: Split on white space Good enough? No Why not? Multi-linguality: Languages without white space delimiters: Chinese, Japanese Agglutinative languages (Finnish) Compounding languages (German) E.g. Lebensversicherungsgesellschaftsangestellter “Life insurance company employee”

57. Tokenization Proposal 1: Split on white space Good enough? No Why not? Multi-linguality: Languages without white space delimiters: Chinese, Japanese Agglutinative languages (Finnish) Compounding languages (German) E.g. Lebensversicherungsgesellschaftsangestellter “Life insurance company employee” Even with English, misses punctuation

58. Tokenization - again Proposal 2: Split on white space and punctuation For English Good enough?

59. Tokenization - again Proposal 2: Split on white space and punctuation For English Good enough? No Problems:

60. Tokenization - again Proposal 2: Split on white space and punctuation For English Good enough? No Problems: Non-splitting punctuation 1.23  1. 23 X; 1,23  1, 23 X don’t  don t X; E-mail  E mail X, etc

61. Tokenization - again Proposal 2: Split on white space and punctuation For English Good enough? No Problems: Non-splitting punctuation 1.23  1. 23 X; 1,23  1, 23 X don’t  don t X; E-mail  E mail X, etc Problems: no-splitting whitespace Names: New York; Collocations: pick up

62. Tokenization - again Proposal 2: Split on white space and punctuation For English Good enough? No Problems: Non-splitting punctuation 1.23  1. 23 X; 1,23  1, 23 X don’t  don t X; E-mail  E mail X, etc Problems: no-splitting whitespace Names: New York; Collocations: pick up What’s a word?

63. Sentence Segmentation Similar issues Proposal: Split on ‘.’ Problems?

64. Sentence Segmentation Similar issues Proposal: Split on ‘.’ Problems? Other punctuation: ?,!,etc Non-boundary periods: 1.23 Mr. Sherword M.p.g. …. Solutions?

65. Sentence Segmentation Similar issues Proposal: Split on ‘.’ Problems? Other punctuation: ?,!,etc Non-boundary periods: 1.23 Mr. Sherword M.p.g. …. Solutions? Rules, dictionaries esp. abbreviations Marked up text + machine learning

66. Homework #1

67. General Notes and Naming All code must run on the CL cluster under Condor Each programming question needs a corresponding condor command file named: E.g. hw1_q1.cmd And should run with: $ condor_submit hw1_q1.cmd General comments should appear in hw1.(pdf|txt) Your code may be tested on new data

68. Q1-Q2: Building and Testing a Rule-Based English Tokenizer Q1: eng_tokenize.* Condor file must contain Input = Output = You may use additional arguments ith input line should correspond to the ith output line Don’t waste too much time making it perfect!! Q2: Explain how your tokenizer handles different phenomena – numbers, hyphenated words, etc – and identify remaining problems.

69. Q3: make_vocab.* Given the input text: The teacher bought the bike. The bike is expensive. The output should be: The 2 the 1 teacher 1 bought 1 bike. 1 bike 1 is 1 expensive. 1

70. Q4: Investigating Tokenization Run programs from Q1, Q3 Compare vocabularies with and without tokenization

71. Next Time Probability Formal languages Finite-state automata