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					     Lecture 1

       CS4705
Introduction to Natural
 Language Processing



         CS 4705
    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
        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?
• 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)
• 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.
               NLP Applications
• Speech Synthesis, Speech Recognition, IVR
  Systems (TOOT: more or less succeeds)
• Information Retrieval (SCANMail demo)
• Information Extraction
  – Question Answering (AQUA)
• Machine Translation (SYSTRAN)
• Summarization (NewsBlaster)
• Automated Psychotherapy (Eliza)
                    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/syllab
  us.html
• 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
                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.
                       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?
                   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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