Description this book offers aunified vision of speech and language processing, presentingstate-of-the-art algorithms and techniques for both speech andtext-based processing of natural language. This comprehensive workcovers both statistical and symbolic approaches to languageprocessing; it shows how they can be applied to important taskssuch as speech recognition, spelling and grammar correction,information extraction, search engines, machine translation, andthe creation of spoken-language dialog agents. For sale in indiansubcontinent only each chapter is built around one or more worked examples demonstrating the main idea of the chapter. Integration of speech and text processing?merges speech processing and natural language processing fields. Covers all of the new statistical approaches, while still completely covering the earlier more structured and rule-based methods. Introduction. I. Words. Regular expressions and automata. Morphology and finite-state transducers. Computational phonology and text-to-speech. Probabilistic models of pronunciation and spelling. N-grams. Hmms and speech recognition. Ii. Syntax. Word classes and part-of-speech tagging. Context-free grammars for english. Parsing with context-free grammars. Features and unification. Lexicalized and probabilistsic parsing. Language and complexity. Iii. Semantics. Representing meaning. Semantic analysis. Lexical semantics. Word sense disambiguation and information retrieval. Iv. Pragmatics. Discourse. Dialogue and conversational agents. Natural language generation. Machine translation. Daniel jurafsky received his ph.d. In computer science from the university ofcalifornia at berkeley. His research focusses on combininglinguistic knowledge with probabilistic models of language andspeech processing in humans and machines. James h. Martin receives his ph.d. In computer science fromthe university of california at berkeley. His research interestsinclude computational semantics, metaphor, machine lear
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