Computational Linguistics INTroduction Lecture 1 Computers and Language.

ComputationalLinguistics INTroduction

Lecture 1

Computers and Language

Feb 2010 -- MR CLINT - Lecture 1 2

Course Information

Course Websitehttp://staff.um.edu.mt/mros1/lin2160

[email protected]@um.edu.mt

Book Jurafsky & Martin, Speech and Language Processing, Prentice Hall 2009, ISBN 978-0-13-504196-3Natural Language Toolkit (NLTK)http://www.nltk.org/


CL: Two Main Disciplines

COMP SCILINGUISTICS language and computers


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


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


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.


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 generates all and only sentences

of a language. Undergeneration Overgeneration

[source: Sag & Wasow]


Generative Power of a Grammar

G

G

GL

L

L

undergenerationonly but not all

overgenerationall but not only

all and only


Formal Grammar

Grammar is a set of rewrite rules Rules have the form

LHS 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


A Simple Grammar/Lexicon

grammar:

S NP VPNP NVP V NPlexicon:

V kicksN JohnN Bill

S

NP

N

John kicks

NPV

VP

N

Bill


Formal v. Natural Languages

Formal Languages

Arithmetic3290 1 1010101

Logicx man(x) mortal(x)

URLhttp://www.cs.um.edu.mt

Natural Languages

EnglishJohn saw the dog

GermanJohann hat den hund gesehen

MalteseĠianni ra kelb


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.


Structure Affects Meaning

I shot an elephant in my trousers


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


Ambiguity Morphological Ambiguity

en-large-ment Lexical Ambiguity

Iraqi Head Seeks Arms 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


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


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)


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.


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.


Information Data Algorithm Program

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


Algorithm for Chocolate Cake


Program to Add X and Y

subtract 1 from X

add 1 to Y

X = 0?

Read X and YX = 2, Y = 3

yesno Output Y


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!


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?


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:


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?


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”


Computers and Language Tools & Resources

Grammar Formalisms, e.g.Definite Clause Grammars

Parsing Algorithmssentence structure

Generation Algorithmsstructure sentence

Statistical Methods Linguistic Corpora


Computers and Language: Applications

Information Retrieval/Extraction Document Classification Question Answering Style and Spell Checking Multimodal Interaction Machine Translation


LECTURES

1 Overview

2 Chomsky Hierarchy

3 Chomsky Hierarchy

4 Chomsky Hierarchy

5 Computational Syntax

6 Agreement & Subcategorisation



9 Corpora, Tools and Techniques

10 Morphology

11 Computational Morphology



14 Revision

Computational Linguistics INTroduction Lecture 1 Computers and Language.

Documents

languageclint lecture

computersclint lecture

wordclint lecture

onlyclint lecture

trousersclint lecture

sag wasowclint lecture

generative grammar

sailorpragmatic ambiguity