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This chapter introduces the field of speechand language processing.

This representation is about a newinterdisciplinary field called computer speechand language processing. The goal of thisnew field is to get computers to performuseful tasks involving human language, taskslike enabling human-machine

communication, improving human-humancommunication, or simply doing usefulprocessing of text or speech.

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We call programs that converse with humansvia natural language conversational agents ordialogue systems. One such example is theHAL. Here we study the various componentsthat make up modern conversational agents.

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What distinguishes language processingapplications from other data processing systemsis their use ofknowledge of language.

For example, HAL must be able to recognize

words from an audio signal and to generate anaudio signal from a sequence of words. Thesetasks ofspeech recognition and speech synthesistasks require knowledge about phonetics andphonology; how words are pronounced in termsof sequences of sounds, and how each of thesesounds is realized acoustically.

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It needs the knowledge oflexical semantics,the meaning of all the words as well ascompositional semantics. We also need toknow something about the relationship of the

words to the syntactic structure.

This knowledge about the kind of actions thatspeakers intend by their use of sentences is

called pragmatic or dialogue knowledge.

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The models and algorithms we present are oftenways to resolve ambiguities that we encounter intext.

Consider the spoken sentence I made her duck. Itmay mean any of the following:

I cooked waterfowl for her.

I cooked waterfowl belonging to her.

I created the toy duck she owns.

I caused her to quickly lower her head or body. I waved my magic wand and turned her into

undifferentiated waterfowl.

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There are many methods of solvingdisambiguities. For example decidingwhether duckis a verb or a noun can besolved by part-of-speech tagging. Decidingwhether makemeans create or cook canbe solved by word sense disambiguation.Resolution of part-of-speech and word senseambiguities are two important kinds oflexicaldisambiguation.

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These various kinds of knowledge can becaptured through the use of a small numberof formal models, or theories. These modelsand theories are all drawn from the standard

toolkits of computer science, mathematics,and linguistics. Among the most importantmodels are state machines, rule systems,logic, probabilistic models, and vector-spacemodels.

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State machines are formal models thatconsist of states, transitions among states,and an input representation. Some of thevariations of this basic model aredeterministic and non-deterministic finite-state automata and finite-state transducers.

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Probabilistic models are crucial for capturingevery kind of linguistic knowledge.

Each of the other models (state machines,formal rule systems, and logic) can beaugmented with probabilities.

The key advantage of probabilistic models istheir ability to solve the many kinds of

ambiguity problems that we discussed earlier.

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The ability of computers to process languageas skilfully as we humans do will signal thearrival of truly intelligent machines.

Turing suggested an empirical test, in whicha computers use of language would form thebasis for determining if it could think.

The task of the machine is to fool the

interrogator into believing it is a person byresponding as a person would to theinterrogators questions.

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Consider the following session with a version of ELIZA: User1: You are like my father in some ways. ELIZA1: WHAT RESEMBLANCE DO YOU SEE User2: You are not very aggressive but I think you dont

want me to notice that.

ELIZA2: WHAT MAKES YOU THINK I AM NOT AGGRESSIVE User3: You dont argue with me. ELIZA3: WHY DO YOU THINK I DONT ARGUE WITH YOU User4: You are afraid of me. ELIZA4: DOES IT PLEASE YOU TO BELIEVE I AM AFRAID OF

YOU ELIZA is a remarkably simple program that makes use of

pattern-matching to process the input and translate it intosuitable outputs. So thats how the answers are given.

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