Foundations of Statistical Natural Language ProcessingMIT Press, 28 de mai. de 1999 - 720 páginas Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications. |
Conteúdo
Mathematical Foundations | 39 |
2 | 59 |
1 | 77 |
Linguistic Essentials | 81 |
2 | 86 |
CorpusBased Work | 117 |
1 | 118 |
issue of The Economist | 131 |
Lexical Acquisition | 265 |
Grammar | 315 |
PartofSpeech Tagging | 341 |
Probabilistic Context Free Grammars | 381 |
Probabilistic Parsing | 407 |
Applications and Techniques | 461 |
Clustering | 495 |
Topics in Information Retrieval | 529 |
4 | 140 |
Words | 149 |
1 | 153 |
ngram Models over Sparse Data | 191 |
Word Sense Disambiguation | 229 |
Text Categorization | 575 |
Tiny Statistical Tables | 609 |
| 657 | |
Outras edições - Ver todos
Foundations of Statistical Natural Language Processing Christopher Manning,Hinrich Schutze Visualização parcial - 1999 |
Foundations of Statistical Natural Language Processing Christopher Manning,Hinrich Schutze Visualização parcial - 1999 |
Foundations of Statistical Natural Language Processing Christopher Manning,Hinrich Schütze Prévia não disponível - 2026 |
Termos e frases comuns
adjectives adverbs algorithm alignment ambiguous word applied approach arg max assume basic Bayes bigrams Brown corpus calculate chapter clustering collocations compute context corpora cross entropy depends derivation dictionary discussed documents EM algorithm English evaluation example Exercise figure frequency function Good-Turing grammar hypothesis language model lexical acquisition linguistics look Markov Model matrix meaning measure methods mutual information n-gram n-gram models node nonterminal normally noun phrase object occur parameters parse tree parser parsing part-of-speech tagging particular PCFG Penn Treebank predict prepositional phrase probability distribution probability estimates problem pronoun random variable refer sample selectional preferences semantic similarity sentence sequence shows space speech Statistical NLP subcategorization frames syntactic tag set tagger theory tion training corpus training data transformation-based translation tree Treebank trigram values vectors verb Viterbi algorithm word sense disambiguation Zipf's law