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C omputational L inguistics INT roduction

C omputational L inguistics INT roduction. Lecture 1 Computers and Language. Course Information. Course Website http://staff.um.edu.mt/mros1/lin2160 Lecturers mike.rosner@um.edu.mt ray.fabri@um.edu.mt

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C omputational L inguistics INT roduction

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  1. ComputationalLinguistics INTroduction Lecture 1 Computers and Language

  2. Course Information • Course Websitehttp://staff.um.edu.mt/mros1/lin2160 • Lecturersmike.rosner@um.edu.mtray.fabri@um.edu.mt • BookJurafsky & Martin, Speech and Language Processing, Prentice Hall 2009, ISBN 978-0-13-504196-3 Natural Language Toolkit (NLTK)http://www.nltk.org/ CLINT - Lecture 1

  3. CL: Two Main Disciplines LINGUISTICS COMP SCI language and computers CLINT - Lecture 1

  4. Language and Computers includes … • Natural Language Processing (NLP) • Computational models of language analysis, interpretation, and generation. • syntax/semantics interface • Human Language Technology • emphasis on large-scale performance • example1: Google search • example2: speech technology • Computational Linguistics • Emphasis on mechanised linguistic theories. • Grew out of early Machine Translation efforts CLINT - Lecture 1

  5. Linguistics • Phonetics: The study of speech sounds • Phonology: The study of sound systems • Morphology: The study of word structure • Syntax: The study of sentence structure • Semantics: The study of meaning • Pragmatics: The study of language use CLINT - Lecture 1

  6. Noam Chomsky • Noam Chomsky’s work in the 1950s radically changed linguistics, making syntax central. • Chomsky has been the dominant figure in linguistics ever since. • Chomsky invented the generative approach to grammar. CLINT - Lecture 1

  7. Generative Grammar:Some Key Points • Theory of grammar includes mathematical definition of what a grammar is. • A language is a (possibly infinite) set of sentences. • But a grammar is finite. • Grammar generatesall and only sentences of a language. • Undergeneration • Overgeneration [source: Sag & Wasow] CLINT - Lecture 1

  8. Generative Power of a Grammar L G G L overgeneration all but not only undergeneration only but not all L G all and only CLINT - Lecture 1

  9. Formal Grammar • Grammar is a set of rewrite rules • Rules have the formLHS  RHS • LHS can be rewritten as RHS • LHS & RHS are sequences made of words or symbols • Lexicon specifies words and their categories Category  word • Category can be rewritten as word CLINT - Lecture 1

  10. NP VP N V NP N John kicks Bill A Simple Grammar/Lexicon grammar: S  NP VP NP  N VP  V NP lexicon: V  kicks N  John N  Bill S CLINT - Lecture 1

  11. Formal Languages Arithmetic3290 1 1010101 Logicx man(x)  mortal(x) URLhttp://www.cs.um.edu.mt Natural Languages EnglishJohn saw the dog GermanJohann hat den hund gesehen MalteseĠianni ra kelb Formal v. Natural Languages CLINT - Lecture 1

  12. Some Points of Similarity • Sentences are sequences of words (or symbols). • Rules determine which sequences are valid sentences. • Sentences have a definite structure. • Sentence structure systematically related to meaning. CLINT - Lecture 1

  13. Structure Affects Meaning I shot an elephant in my trousers CLINT - Lecture 1

  14. Formal Languages The grammar defines the language Restricted application Non ambiguous Natural Languages The language defines the grammar Universal application Highly ambiguous Points of Difference CLINT - Lecture 1

  15. Ambiguity • Morphological Ambiguityen-large-ment • Lexical AmbiguityIraqi Head Seeks Arms • Syntactic Ambiguitysmall animals and children laugh • Semantic Ambiguityevery girl loves a sailor • Pragmatic Ambiguitycan you pass the salt? • The management of ambiguity is central to the success of CL CLINT - Lecture 1

  16. I made her duck • I cooked a duck for her • I cooked a duck belonging to her • I created a duck for her • I created a duck that now belongs to her • I caused her to lower her head • I turned her into a duck CLINT - Lecture 1

  17. Computer Science • The study of basic concepts • Information • Data • Algorithm • Program • The application of these concepts to practical tasks. • Implementation of computational models from other fields (meteorology,..,linguistics) CLINT - Lecture 1

  18. Information Data Algorithm Program • Information is a theoretical concept invented by Shannon in 1948 to measure uncertainty. The units of this measure are called bits. • Length – metres • Weight – kilos • Information – bits • 1 bit is the amount of uncertainty inherent to a situation when there are exactly two possible outcomes. Example: for breakfast I will have coffee or I will have tea (nothing else). • When I tell you that I have tea, I have conveyed one bit of information. • The greater the number of possible outcomes, the more bits of infomation involved in the statement that indicates the actual outcome. CLINT - Lecture 1

  19. Information DataAlgorithm Program • A formalized representation of facts or concepts suitable for communication, interpretation, or processing by people or automated means. • Example: a telephone directory • Unlike information, which is abstract, data is concrete • Data has a certain level of structure. In the telephone directory, for example, we have the structure of a list of entries, each of which has a name, an address, and a number. CLINT - Lecture 1

  20. InformationData AlgorithmProgram A completely defined procedure for the solution of a given problem in a finite number of steps • Designed for a well-defined task. • Finite description length. • Guaranteed to terminate. • Abstract CLINT - Lecture 1

  21. Algorithm for Chocolate Cake CLINT - Lecture 1

  22. X = 0? Program to Add X and Y Read X and Y X = 2, Y = 3 subtract 1 from X add 1 to Y Output Y no yes CLINT - Lecture 1

  23. Computer Program A set of instructions, written in a specific programming language, which a computer follows in processing data, performing an operation, or solving a logical problem. • Concrete • A program can implement an algorithm. • More than one program may implement the same algorithm. • Not all programs express good algorithms! CLINT - Lecture 1

  24. Instructions vs. Execution Steps • Read X • Read Y • X = X-1 • Y = Y+1 • If X = 0 then Print(X) else goto 3 How many instructions? How many execution steps? CLINT - Lecture 1

  25. Algorithms and Linguistics • Do linguistic theories in the abstract make sense? • Linguistic theory explain linguistic knowledge in the form of • grammar rules • theories about grammar rules • But performance, involves processing issues: CLINT - Lecture 1

  26. Computational Linguistics – Issues • How are a grammar and a lexicon represented? • How is the structure of a given sentence actually discovered? • How can we actually generate a sentence to express a particular intended meaning? • How can linguistic theory be made concrete enough to test algorithmically? • Can an artificial system learn a language with limited exposure to grammatical sentences? CLINT - Lecture 1

  27. Computers and LanguageTwin Goals • Scientific Goal:Contribute to Linguistics by adding a computational dimension. • Technological Goal: Develop machinery capable of handling human language that can support “language engineering” CLINT - Lecture 1

  28. Computers and Language Tools & Resources • Grammar Formalisms, e.g.Definite Clause Grammars • Parsing Algorithmssentence  structure • Generation Algorithmsstructure  sentence • Statistical Methods • Linguistic Corpora CLINT - Lecture 1

  29. Computers and Language: Applications • Information Retrieval/Extraction • Document Classification • Question Answering • Style and Spell Checking • Multimodal Interaction • Machine Translation CLINT - Lecture 1

  30. LECTURES CLINT - Lecture 1

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