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Introduction to Natural Language Processing

Explore the study of human languages and how they can be represented, analyzed, and generated algorithmically. Learn about morphology, phonetics, syntax, semantics, pragmatics, and discourse. Discover the applications of NLP in speech synthesis, information retrieval, question answering, machine translation, summarization, and more.

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Introduction to Natural Language Processing

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  1. Lecture 1 CS4705 Introduction to Natural Language Processing CS 4705

  2. What is Natural Language Processing? • The study of human languages and how they can be represented computationally and analyzed and generated algorithmically • The cat is on the mat. --> on (mat, cat) • on (mat, cat) --> The cat is on the mat • Studying NLP involves studying natural language, formal representations, and algorithms for their manipulation

  3. What can we learn about language? • Morphology: words and their composition • cat, cats, dogs • child, children • undo, union • Phonetics and Phonology: speech sounds, their production, and the rule systems that govern their use • tap, butter • nice white rice; height/hot; kite/cot; night/not... • city hall, parking lot, city hall parking lot • The cat is on the mat. The cat is on the mat?

  4. Syntax: the structuring of words into larger phrases • John hit Bill • Bill was hit by John (passive) • Bill, John hit (preposing) • Who John hit was Bill (wh-cleft) • Semantics: the (truth-functional) meaning of words and phrases • gun(x) & holster(y) & in(x,y) • fake (gun (x)) (compositional semantics) • The king of France is bald (presupposition violation) • bass fishing, bass playing (word sense disambiguation)

  5. Pragmatics and Discourse: the meaning of words and phrases in context • George got married and had a baby. • George had a baby and got married. • Some people left early. • Prosodic Variation • German teachers • Bill doesn’t drink because he’s unhappy. • John only introduced Mary to Sue. • John called Bill a Republican and then he insulted him. • John likes his mother, and so does Bill.

  6. NLP Applications • Speech Synthesis, Speech Recognition, IVR Systems (TOOT: moreor less succeeds) • Information Retrieval (SCANMail demo) • Information Extraction • Question Answering (AQUA) • Machine Translation (SYSTRAN) • Summarization (NewsBlaster) • Automated Psychotherapy (Eliza)

  7. Bureaucracy • Instructor: Julia Hirschberg • Office and hours: CEPSR 705, TTh 2:30-3:30 • Teaching Assistant: Jackson Liscombe • Office and hours: CEPSR 702, M 2-3; W 1-2 • Syllabus available at http://www1.cs.columbia.edu/~julia/cs4705/syllabus.html

  8. Text: Daniel Jurafsky and James H. Martin, Speech and Language Processing, Prentice-Hall, 2000 (available at Platypus Books) Note errata available on website; check before reading each chapter please • Assignments: 3 homework assignments, midterm, final • Evaluation: 40% homework + 40% exams + 20% class participation

  9. Academic Integrity Copying or paraphrasing someone's work (code included), or permitting your own work to be copied or paraphrased, even if only in part, is forbidden, and will result in an automatic grade of 0 for the entire assignment or exam in which the copying or paraphrasing was done. Your grade should reflect your own work. If you are going to have trouble completing an assignment, talk to the instructor or TA in advance of the due date please. Everyone: Read/write protect your homework files at all times.

  10. Questions • Name • Email address • Undergrad/Grad • Major/Specialization • Previous language study • Natural Languages • Your native language • Languages you are fluent in • Languages you have some facility in • Anything else?

  11. For Next Class • Read Chapters 1-2 • For fun: Experiment with Eliza: • Does she pass the Turing Test? • What kind of input defeats her? • How could you improve her ability to fool people into thinking she is human?

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