Foundations of statistical natural language processing / Christopher D. Manning

Public ISBD Titre : Foundations of statistical natural language processing Type de document : texte imprimé Auteurs : Christopher D. Manning ; Hinrich SchLutze Editeur : Cambridge, Mass. : MIT Press Année de publication : 1999 Importance : 1 vol. (680 p.) Présentation : ill., couv. ill. Format : 24 cm ISBN/ISSN/EAN : 978-0-262-13360-9 Catégories : Mathématique Mots-clés : Linguistique : Informatique : Méthodes statistiques Traitement automatique du langage naturel Index. décimale : 410 Linguistique générale Résumé : Les approches statistiques du traitement du texte en langage naturel sont devenues dominantes ces dernières années. Ce texte fondamental est la première introduction complète au traitement statistique du langage naturel (PNL) à apparaître. Le livre contient toute la théorie et les algorithmes nécessaires pour construire des outils PNL. Il fournit une couverture large mais rigoureuse des fondements mathématiques et linguistiques, ainsi qu'une discussion détaillée des méthodes statistiques, permettant aux étudiants et aux chercheurs de construire leurs propres implémentations. Le livre couvre la recherche de collocation, la désambiguïsation du sens du mot, l'analyse probabiliste, la recherche d'information et d'autres applications. Note de contenu : Sommaire Preliminaries Introduct Mathematical Foundations Linguistic Essentials Corpus-Based Work Words Collocations Statistical Inference: n-gram Models over Sparse Data Word Sense Disambiguation Lexical Acquisition Grammar Markov Models Part-of-Speech Tagging Probabilistic Context Free Grammars Probabilistic Parsing Applications and Techniques Statistical Alignment and Machine Translation Clustering Topics in Information Retrieval Text Categorization Côte titre : Fs/19755 Foundations of statistical natural language processing [texte imprimé] / Christopher D. Manning ; Hinrich SchLutze . - Cambridge, Mass. : MIT Press, 1999 . - 1 vol. (680 p.) : ill., couv. ill. ; 24 cm. ISBN : 978-0-262-13360-9 Catégories : Mathématique Mots-clés : Linguistique : Informatique : Méthodes statistiques Traitement automatique du langage naturel Index. décimale : 410 Linguistique générale Résumé : Les approches statistiques du traitement du texte en langage naturel sont devenues dominantes ces dernières années. Ce texte fondamental est la première introduction complète au traitement statistique du langage naturel (PNL) à apparaître. Le livre contient toute la théorie et les algorithmes nécessaires pour construire des outils PNL. Il fournit une couverture large mais rigoureuse des fondements mathématiques et linguistiques, ainsi qu'une discussion détaillée des méthodes statistiques, permettant aux étudiants et aux chercheurs de construire leurs propres implémentations. Le livre couvre la recherche de collocation, la désambiguïsation du sens du mot, l'analyse probabiliste, la recherche d'information et d'autres applications. Note de contenu : Sommaire Preliminaries Introduct Mathematical Foundations Linguistic Essentials Corpus-Based Work Words Collocations Statistical Inference: n-gram Models over Sparse Data Word Sense Disambiguation Lexical Acquisition Grammar Markov Models Part-of-Speech Tagging Probabilistic Context Free Grammars Probabilistic Parsing Applications and Techniques Statistical Alignment and Machine Translation Clustering Topics in Information Retrieval Text Categorization Côte titre : Fs/19755

Exemplaires (1)

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Introduction to information retrieval / Christopher D. Manning

Public ISBD Titre : Introduction to information retrieval Type de document : texte imprimé Auteurs : Christopher D. Manning, Auteur ; Prabhakar Raghavan, Auteur ; Hinrich Schütze, Auteur Editeur : Cambridge : Cambridge university press Année de publication : 2008 Importance : 1 vol. (482 p.) Présentation : ill. Format : 27 cm ISBN/ISSN/EAN : 978-0-521-86571-5 Note générale : Bibliogr. p. 441-468 Langues : Anglais (eng) Catégories : Informatique Mots-clés : Traitement de texte (Informatique) Récupération de l'information Clustering de documents Web sémantique Recherche de l'information Web sémantique Discours (linguistique) Index. décimale : 025.04 Systèmes de recherche et stockage de l'information Résumé : Testé en classe et cohérent, ce manuel enseigne la recherche d'informations classiques et sur le Web, y compris la recherche sur le Web et les domaines connexes de la classification des textes et de la classification des textes à partir des concepts de base. Il fournit un traitement à jour de tous les aspects de la conception et de la mise en Å“uvre de systèmes de collecte, d'indexation et de recherche de documents; méthodes d'évaluation des systèmes; et une introduction à l'utilisation des méthodes d'apprentissage automatique sur les collections de textes. Toutes les idées importantes sont expliquées à l'aide d'exemples et de chiffres, ce qui en fait un outil idéal pour les cours d'introduction à la recherche d'information pour les étudiants de premier cycle avancés et les étudiants diplômés en informatique. Basé sur les retours d'expérience de la salle de classe, le livre a été soigneusement structuré afin de rendre l'enseignement plus naturel et efficace. Des diapositives et des exercices supplémentaires (avec des solutions pour les conférenciers) sont également disponibles sur le site Web de soutien du livre pour aider les instructeurs de cours à préparer leurs conférences. Note de contenu : Sommaire Boolean retrieval An example information retrieval problem A first take at building an inverted index Processing Boolean queries The extended Boolean model versus ranked retrieval References and further reading The term vocabulary and postings lists Document delineation and character sequence decoding Obtaining the character sequence in a document Choosing a document unit Determining the vocabulary of terms Tokenization Dropping common terms: stop words Normalization (equivalence classing of terms) Stemming and lemmatization Faster postings list intersection via skip pointers Positional postings and phrase queries Biword indexes Positional indexes Combination schemes References and further reading Dictionaries and tolerant retrieval Search structures for dictionaries Wildcard queries General wildcard queries Permuterm indexes k-gram indexes for wildcard queries Spelling correction Implementing spelling correction Forms of spelling correction Edit distance k-gram indexes for spelling correction Context sensitive spelling correction Phonetic correction References and further reading Index construction Hardware basics Blocked sort-based indexing Single-pass in-memory indexing Distributed indexing Dynamic indexing Other types of indexes References and further reading Index compression Statistical properties of terms in information retrieval Heaps' law: Estimating the number of terms Zipf's law: Modeling the distribution of terms Dictionary compression Dictionary as a string Blocked storage Postings file compression Variable byte codes Gamma codes References and further reading Scoring, term weighting and the vector space model Parametric and zone indexes Weighted zone scoring Learning weights The optimal weight g Term frequency and weighting Inverse document frequency Tf-idf weighting The vector space model for scoring Dot products Queries as vectors Computing vector scores Variant tf-idf functions Sublinear tf scaling Maximum tf normalization Document and query weighting schemes Pivoted normalized document length References and further reading Computing scores in a complete search system Efficient scoring and ranking Inexact top K document retrieval Index elimination Champion lists Static quality scores and ordering Impact ordering Cluster pruning Components of an information retrieval system Tiered indexes Query-term proximity Designing parsing and scoring functions Putting it all together Vector space scoring and query operator interaction Boolean retrieval Wildcard queries Phrase queries References and further reading Evaluation in information retrieval Information retrieval system evaluation Standard test collections Evaluation of unranked retrieval sets Evaluation of ranked retrieval results Assessing relevance Critiques and justifications of the concept of relevance A broader perspective: System quality and user utility System issues User utility Refining a deployed system Results snippets References and further reading Relevance feedback and query expansion Relevance feedback and pseudo relevance feedback The Rocchio algorithm for relevance feedback Probabilistic relevance feedback When does relevance feedback work? Relevance feedback on the web Evaluation of relevance feedback strategies Pseudo relevance feedback Indirect relevance feedback Summary Global methods for query reformulation Vocabulary tools for query reformulation Query expansion Automatic thesaurus generation References and further reading XML retrieval Basic XML concepts Challenges in XML retrieval A vector space model for XML retrieval Evaluation of XML retrieval Text-centric vs. data-centric XML retrieval References and further reading Exercises Probabilistic information retrieval Review of basic probability theory The Probability Ranking Principle The 1/0 loss case The PRP with retrieval costs The Binary Independence Model Deriving a ranking function for query terms Probability estimates in theory Probability estimates in practice Probabilistic approaches to relevance feedback An appraisal and some extensions An appraisal of probabilistic models Tree-structured dependencies between terms Okapi BM25: a non-binary model Bayesian network approaches to IR References and further reading Language models for information retrieval Language models Finite automata and language models Types of language models Multinomial distributions over words The query likelihood model Using query likelihood language models in IR Estimating the query generation probability Ponte and Croft's Experiments Language modeling versus other approaches in IR Extended language modeling approaches References and further reading Text classification and Naive Bayes The text classification problem Naive Bayes text classification Relation to multinomial unigram language model The Bernoulli model Properties of Naive Bayes A variant of the multinomial model Feature selection Mutual information $\chi ^2$ Feature selectionChi2 Feature selection Assessing $\chi ^2$ as a feature selection methodAssessing chi-square as a feature selection method Frequency-based feature selection Feature selection for multiple classifiers Comparison of feature selection methods Evaluation of text classification References and further reading Vector space classification Document representations and measures of relatedness in vector spaces Rocchio classification k nearest neighbor Time complexity and optimality of kNN Linear versus nonlinear classifiers Classification with more than two classes The bias-variance tradeoff References and further reading Exercises Support vector machines and machine learning on documents Support vector machines: The linearly separable case Extensions to the SVM model Soft margin classification Multiclass SVMs Nonlinear SVMs Experimental results Issues in the classification of text documents Choosing what kind of classifier to use Improving classifier performance Large and difficult category taxonomies Features for text Document zones in text classification Machine learning methods in ad hoc information retrieval A simple example of machine-learned scoring Result ranking by machine learning References and further reading Flat clustering Clustering in information retrieval Problem statement Cardinality - the number of clusters Evaluation of clustering K-means Cluster cardinality in K-means Model-based clustering References and further reading Exercises Hierarchical clustering Hierarchical agglomerative clustering Single-link and complete-link clustering Time complexity of HAC Group-average agglomerative clustering Centroid clustering Optimality of HAC Divisive clustering Cluster labeling Implementation notes References and further reading Exercises Matrix decompositions and latent semantic indexing Linear algebra review Matrix decompositions Term-document matrices and singular value decompositions Low-rank approximations Latent semantic indexing References and further reading Web search basics Background and history Web characteristics The web graph Spam Advertising as the economic model The search user experience User query needs Index size and estimation Near-duplicates and shingling References and further reading Web crawling and indexes Overview Features a crawler must provide Features a crawler should provide Crawling Crawler architecture Distributing the crawler DNS resolution The URL frontier Distributing indexes Connectivity servers References and further reading Link analysis The Web as a graph Anchor text and the web graph PageRank Markov chains The PageRank computation Topic-specific PageRank Hubs and Authorities Choosing the subset of the Web References and further reading Bibliography Index Côte titre : Fs/19776 Introduction to information retrieval [texte imprimé] / Christopher D. Manning, Auteur ; Prabhakar Raghavan, Auteur ; Hinrich Schütze, Auteur . - Cambridge : Cambridge university press, 2008 . - 1 vol. (482 p.) : ill. ; 27 cm. ISBN : 978-0-521-86571-5 Bibliogr. p. 441-468 Langues : Anglais (eng) Catégories : Informatique Mots-clés : Traitement de texte (Informatique) Récupération de l'information Clustering de documents Web sémantique Recherche de l'information Web sémantique Discours (linguistique) Index. décimale : 025.04 Systèmes de recherche et stockage de l'information Résumé : Testé en classe et cohérent, ce manuel enseigne la recherche d'informations classiques et sur le Web, y compris la recherche sur le Web et les domaines connexes de la classification des textes et de la classification des textes à partir des concepts de base. Il fournit un traitement à jour de tous les aspects de la conception et de la mise en Å“uvre de systèmes de collecte, d'indexation et de recherche de documents; méthodes d'évaluation des systèmes; et une introduction à l'utilisation des méthodes d'apprentissage automatique sur les collections de textes. Toutes les idées importantes sont expliquées à l'aide d'exemples et de chiffres, ce qui en fait un outil idéal pour les cours d'introduction à la recherche d'information pour les étudiants de premier cycle avancés et les étudiants diplômés en informatique. Basé sur les retours d'expérience de la salle de classe, le livre a été soigneusement structuré afin de rendre l'enseignement plus naturel et efficace. Des diapositives et des exercices supplémentaires (avec des solutions pour les conférenciers) sont également disponibles sur le site Web de soutien du livre pour aider les instructeurs de cours à préparer leurs conférences. Note de contenu : Sommaire Boolean retrieval An example information retrieval problem A first take at building an inverted index Processing Boolean queries The extended Boolean model versus ranked retrieval References and further reading The term vocabulary and postings lists Document delineation and character sequence decoding Obtaining the character sequence in a document Choosing a document unit Determining the vocabulary of terms Tokenization Dropping common terms: stop words Normalization (equivalence classing of terms) Stemming and lemmatization Faster postings list intersection via skip pointers Positional postings and phrase queries Biword indexes Positional indexes Combination schemes References and further reading Dictionaries and tolerant retrieval Search structures for dictionaries Wildcard queries General wildcard queries Permuterm indexes k-gram indexes for wildcard queries Spelling correction Implementing spelling correction Forms of spelling correction Edit distance k-gram indexes for spelling correction Context sensitive spelling correction Phonetic correction References and further reading Index construction Hardware basics Blocked sort-based indexing Single-pass in-memory indexing Distributed indexing Dynamic indexing Other types of indexes References and further reading Index compression Statistical properties of terms in information retrieval Heaps' law: Estimating the number of terms Zipf's law: Modeling the distribution of terms Dictionary compression Dictionary as a string Blocked storage Postings file compression Variable byte codes Gamma codes References and further reading Scoring, term weighting and the vector space model Parametric and zone indexes Weighted zone scoring Learning weights The optimal weight g Term frequency and weighting Inverse document frequency Tf-idf weighting The vector space model for scoring Dot products Queries as vectors Computing vector scores Variant tf-idf functions Sublinear tf scaling Maximum tf normalization Document and query weighting schemes Pivoted normalized document length References and further reading Computing scores in a complete search system Efficient scoring and ranking Inexact top K document retrieval Index elimination Champion lists Static quality scores and ordering Impact ordering Cluster pruning Components of an information retrieval system Tiered indexes Query-term proximity Designing parsing and scoring functions Putting it all together Vector space scoring and query operator interaction Boolean retrieval Wildcard queries Phrase queries References and further reading Evaluation in information retrieval Information retrieval system evaluation Standard test collections Evaluation of unranked retrieval sets Evaluation of ranked retrieval results Assessing relevance Critiques and justifications of the concept of relevance A broader perspective: System quality and user utility System issues User utility Refining a deployed system Results snippets References and further reading Relevance feedback and query expansion Relevance feedback and pseudo relevance feedback The Rocchio algorithm for relevance feedback Probabilistic relevance feedback When does relevance feedback work? Relevance feedback on the web Evaluation of relevance feedback strategies Pseudo relevance feedback Indirect relevance feedback Summary Global methods for query reformulation Vocabulary tools for query reformulation Query expansion Automatic thesaurus generation References and further reading XML retrieval Basic XML concepts Challenges in XML retrieval A vector space model for XML retrieval Evaluation of XML retrieval Text-centric vs. data-centric XML retrieval References and further reading Exercises Probabilistic information retrieval Review of basic probability theory The Probability Ranking Principle The 1/0 loss case The PRP with retrieval costs The Binary Independence Model Deriving a ranking function for query terms Probability estimates in theory Probability estimates in practice Probabilistic approaches to relevance feedback An appraisal and some extensions An appraisal of probabilistic models Tree-structured dependencies between terms Okapi BM25: a non-binary model Bayesian network approaches to IR References and further reading Language models for information retrieval Language models Finite automata and language models Types of language models Multinomial distributions over words The query likelihood model Using query likelihood language models in IR Estimating the query generation probability Ponte and Croft's Experiments Language modeling versus other approaches in IR Extended language modeling approaches References and further reading Text classification and Naive Bayes The text classification problem Naive Bayes text classification Relation to multinomial unigram language model The Bernoulli model Properties of Naive Bayes A variant of the multinomial model Feature selection Mutual information $\chi ^2$ Feature selectionChi2 Feature selection Assessing $\chi ^2$ as a feature selection methodAssessing chi-square as a feature selection method Frequency-based feature selection Feature selection for multiple classifiers Comparison of feature selection methods Evaluation of text classification References and further reading Vector space classification Document representations and measures of relatedness in vector spaces Rocchio classification k nearest neighbor Time complexity and optimality of kNN Linear versus nonlinear classifiers Classification with more than two classes The bias-variance tradeoff References and further reading Exercises Support vector machines and machine learning on documents Support vector machines: The linearly separable case Extensions to the SVM model Soft margin classification Multiclass SVMs Nonlinear SVMs Experimental results Issues in the classification of text documents Choosing what kind of classifier to use Improving classifier performance Large and difficult category taxonomies Features for text Document zones in text classification Machine learning methods in ad hoc information retrieval A simple example of machine-learned scoring Result ranking by machine learning References and further reading Flat clustering Clustering in information retrieval Problem statement Cardinality - the number of clusters Evaluation of clustering K-means Cluster cardinality in K-means Model-based clustering References and further reading Exercises Hierarchical clustering Hierarchical agglomerative clustering Single-link and complete-link clustering Time complexity of HAC Group-average agglomerative clustering Centroid clustering Optimality of HAC Divisive clustering Cluster labeling Implementation notes References and further reading Exercises Matrix decompositions and latent semantic indexing Linear algebra review Matrix decompositions Term-document matrices and singular value decompositions Low-rank approximations Latent semantic indexing References and further reading Web search basics Background and history Web characteristics The web graph Spam Advertising as the economic model The search user experience User query needs Index size and estimation Near-duplicates and shingling References and further reading Web crawling and indexes Overview Features a crawler must provide Features a crawler should provide Crawling Crawler architecture Distributing the crawler DNS resolution The URL frontier Distributing indexes Connectivity servers References and further reading Link analysis The Web as a graph Anchor text and the web graph PageRank Markov chains The PageRank computation Topic-specific PageRank Hubs and Authorities Choosing the subset of the Web References and further reading Bibliography Index Côte titre : Fs/19776

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
Fs/19776	Fs/19776	Livre	Bibliothéque des sciences	Français	Disponible Disponible

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