This textbook is intended for graduate students in computer science and linguistics who are interested in developing expertise in natural language processing (NLP) and in those aspects of artificial intelligence which are concerned with computer models oflanguage comprehension. The text is somewhat different from a number of other excellent textbooks in that its foci are more on the linguistic and psycho linguistic prerequisites and on foundational issues concerning human linguistic behavior than on the description of the extant models and algorithms. The goal is to make the student, undertaking the enormous task of developing computer models for NLP, well aware of the major diffi culties and unsolved problems, so that he or she will not begin the task (as it has often been done) with overoptimistic hopes or claims about the generalizability of models, when such hopes and claims are incon sistent either with some aspects of the formal theory or with known facts about human cognitive behavior. Thus, I try to enumerate and explain the variety of cognitive, linguistic, and pragmatic data which must be understood and formalized before they can be incorporated into a computer model.
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I • Preliminaries and Prerequisites.- 1: Formal Languages and Automata.- Grammars and Languages.- Types of Grammars.- Finite-State Grammars.- Context-Free Grammars.- Recognizers and Acceptor Automata.- Finite-State Automata.- Pushdown Automata.- Turing Machines.- Linear Bounded Automata.- Syntax-Directed Translation.- Translation Machines.- Parsing Algorithms.- Exercises.- References.- 2: Linguistics.- Modern Linguistics.- Transformational-Generative Linguistics.- Grammars for Natural Languages.- The Standard Theory.- Extension of the Standard Theory.- Revised Extended Standard Theory (REST).- Government and Binding: GB Linguistics.- Concluding Remarks.- Exercises.- Topics for Research and Term Papers.- References.- 3: Psycholinguistics.- Lexical Processing.- Sentence Processing.- Cognitive Psychology and Memory.- Conceptual Comprehension.- Exercises.- Topics for Research and Term Papers.- References.- II • Views and Reviews.- 4: Language Comprehension.- Language Perception.- Sentence-Processing Mechanism.- Text-Processing Mechanism.- Exercises.- References.- 5: Grammars and Parsing Strategies.- Lexical Functional Grammar.- The Sausage Machine Parser.- The “Strength” of Lexical Form.- ATN Grammars and Parsers.- Determinism Hypothesis.- Finite-State Parsing.- Exercises.- References.- 6: Semantics.- The Truth-Conditional Theory.- The Sense Theory.- Speech Act.- Sentence Verification and Propositional Models.- Meaning in Different Worlds.- Exercises.- References.- 7: Natural Language Understanding: The Artificial Intelligence Approach.- Views on Artificial Intelligence.- Common Sense and Learning.- Tools of Natural Language Processing.- Models of Natural Language Processing.- Exercises.- References.- III • Construction of a Language Processor.- 8: Lexical Processing.- What Should Be in the Lexicon?.- Word Slots and Pattern Recognition.- The Lexicon in Generative Grammar.- Phrasal Dictionary.- Exercises.- References.- 9: Syntactic Processing.- A Simple Nondeterministic Parser.- Some Modifications of Algorithm M and JtE.- The Size of the Observation Window.- Toward a Model of Human Language Understanding.- Exercises.- References.- Name Index.
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