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Common terms and phrasesacoustic algorithm alignment ambiguity applied approach automaton bigram Chapter classifier compute constituent constraints context context-free context-free grammars corpus decoding defined definition dialogue difficult diphone disambiguation discourse documents English evaluation example extraction feature structures field Figure final find finite finite-state first flight frequency given grammar hidden Markov model input introduced labeled language model language processing lexical lexicon likelihood linguistic machine machine learning Markov match meaning representation modified morpheme morphological N-gram natural language node Nominal non-terminal noun phrase parse tree parser part-of-speech tagging predict probabilistic probability problem pronoun reference regular expression regular languages relations represent role rules segmentation semantic sense sentence sequence shows specific speech and language speech recognition string suffix summarization symbols syntactic tagger tagset task training set transducer translation Treebank unification values vector verb Viterbi Viterbi algorithm vowel word WordNet References to this bookFrom Google ScholarFoundations of Statistical Natural Language ProcessingChristopher D Manning, Hinrich Schutze - Computational Linguistics Predicting Subcellular Localization of Proteins using Machine ...Z Lu, D Szafron, R Greiner, P Lu, DS Wishart, B Poulin, J Anvik, C Macdonell, R Eisner - Bioinformatics Anaphora and Discourse StructureBonnie Webber, Matthew Stone, Aravind Joshi, Alistair Knott - 2003 - Computational Linguistics An Improved Error Model for Noisy Channel Spelling CorrectionEric Brill, Robert C Moore Bibliographic information |