1. Introduction to Computational Linguistics (LIN3060) Lecture 1 Computers and Language

2. Feb 2005 -- MRCLINT - Lecture 12 Course Information Web http://www.cs.um.edu.mt/~mros/lin 3060 Lecturers mike.rosner@um.edu.mt ray.fabri@um.edu.mt Books Speech and Language Processing, Jurafsky and Martin, Prentice Hall 2000 Algorithmics, David Harel, Addison Wesley, 2004

3. Feb 2005 -- MRCLINT - Lecture 13 Computers and Language Computational Linguistics Emphasis on mechanised linguistic theories. Grew out of early Machine Translation efforts Natural Language Processing Computational models of language analysis, interpretation, and generation. Language Engineering emphasis on large-scale performance example: Google

4. Feb 2005 -- MRCLINT - Lecture 14 CL: Two Main Disciplines COMP SCILINGUISTICS

5. Feb 2005 -- MRCLINT - Lecture 15 Linguistics is Multi Layered 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

6. Feb 2005 -- MRCLINT - Lecture 16 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.

7. Feb 2005 -- MRCLINT - Lecture 17 Generative Grammar is Prescriptive Prescriptive Grammar Rules for and against certain uses Proscribed forms that are in current use “don’t end a sentence with a preposition” Subjective Descriptive Grammar Rules characterizing what people actually say Goal to characterize all and only that which speakers find acceptable Objective

8. Feb 2005 -- MRCLINT - Lecture 18 Generative Grammar: Key Points A language is a (possibly infinite) set of sentences. Grammar is finite. Grammar of a particular language expresses linguistic knowledge of that language Theory of Grammar includes mathematical definition of what a grammar is. The “Theory of Grammar” is a theory of human linguistic abilities. [source: Sag & Wasow]

9. Feb 2005 -- MRCLINT - Lecture 19 Theories of Sentence and Word Structure: Rewrite Rules Rules can be used to specify the sentences of a language. Rules have the form LHS  RHS LHS may be a sequence of symbols RHS may be a sequence of symbols or words. Lexicon specifies words and their categories

10. Feb 2005 -- MRCLINT - Lecture 110 A Simple Grammar/Lexicon grammar: S  NP VP NP  N VP  V NP lexicon: V  kicks N  John N  Bill S NP N Johnkicks NPV VP N Bill

11. Feb 2005 -- MRCLINT - Lecture 111 Formal v. Natural Languages Formal Languages Arithmetic 3290 1 1010101 Logic  x man(x)  mortal(x) URL http://www.cs.um.edu.mt Natural Languages English John saw the dog German Johann hat den hund gesehen Maltese Ġianni ra kelb

12. Feb 2005 -- MRCLINT - Lecture 112 Points of Similarity A language is considered to be a (possibly infinite) set of sentences. Sentences are sequences of words. Rules determine which sequences are valid sentences. Sentences have a definite structure. Sentence structure related to meaning.

13. Feb 2005 -- MRCLINT - Lecture 113 Points of Difference Formal Languages The grammar defines the language Restricted application Non ambiguous Natural Languages The language defines the grammar Universal application Highly ambiguous

14. Feb 2005 -- MRCLINT - Lecture 114 Ambiguity Morphological Ambiguity en-large-ment Lexical Ambiguity the sheep is in the pen Syntactic Ambiguity small animals and children laugh Semantic Ambiguity every girl loves a sailor Pragmatic Ambiguity can you pass the salt? The management of ambiguity is central to the success of CL in general and MT in particular.

15. Feb 2005 -- MRCLINT - Lecture 115 Computer Science The study of basic concepts Information Data Algorithm Program The application of these concepts to practical tasks. Implementation of computational models.

16. Feb 2005 -- MRCLINT - Lecture 116 Information Information is an 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.

17. Feb 2005 -- MRCLINT - Lecture 117 Data 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.

18. Feb 2005 -- MRCLINT - Lecture 118 Algorithm

19. Feb 2005 -- MRCLINT - Lecture 119 Algorithm Input: ingredients Output: delicious chocolate cake Method: Algorithm Hardware: oven, pan, chef Software: recipe

20. Feb 2005 -- MRCLINT - Lecture 120 Algorithm to Add X and Y subtract 1 from X add 1 to Y X = 0? Read X and Y X = 2, Y = 3 yesno Output Y

21. Feb 2005 -- MRCLINT - Lecture 121 Algorithm A well defined procedure for the solution of a given problem in a finite number of steps Abstract Designed to perform a well-defined task. Finite description length. Guaranteed to terminate.

22. Feb 2005 -- MRCLINT - Lecture 122 Levels of Detail Every algorithm assumes the existence of elementary instructions, e.g. spread the ingredients in the pan add 1 to Y The idea is that these can be executed by the hardware directly. There is nothing necessary about the particular instruction set. We could imagine greater or lesser amounts of detail. We need to agree about the instruction set before describing an algorithm.

23. Feb 2005 -- MRCLINT - Lecture 123 Abstraction Every algorithm could be described at the lowest level of detail. However, the process of abstracting away from the elementary details is central to efficient description – for computers as well as humans. Prepare a sauce bordelaise and pour over the meat. Computer programming languages embody higher levels of abstraction and allow more efficient descriptions

24. Feb 2005 -- MRCLINT - Lecture 124 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.

25. Feb 2005 -- MRCLINT - Lecture 125 Instructions vs. Execution Steps 1.Read X 2.Read Y 3.X = X-1 4.Y = Y+1 5.If X = 0 then Print(X) else goto 3 How many instructions? How many execution steps?

26. Feb 2005 -- MRCLINT - Lecture 126 Computer Program Finite Length Concrete (can be written down) Implements an algorithm. More than one program may implement the same algorithm. Not all programs express good algorithms!

27. Feb 2005 -- MRCLINT - Lecture 127 Algorithms and Linguistics Linguistic theory provides linguistic knowledge in the form of grammar rules theories about grammar rules Putting knowledge to some use involves processing, e.g.: parsing generation

28. Feb 2005 -- MRCLINT - Lecture 128 Computational Linguistics – Issues How are a grammar and a lexicon represented? By what algorithm can we actually discover the structure of a sentence? actually generate a sentence to express a particular meaning? How can we actually test a linguistic theory? Could an artificial system acquire a grammar with limited exposure to grammatical sentences.

29. Feb 2005 -- MRCLINT - Lecture 129 Computers and Language Twin 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”

30. Feb 2005 -- MRCLINT - Lecture 130 Computers and Language: Applications Information Retrieval/Extraction Document Classification Question Answering Style and Spell Checking Integrated Multimodal Tasks Machine Translation