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Learning to Rank for Information Retrieval and Natural Language Processing

ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed.;

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Graph-based Natural Language Processing and Information Retrieval

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these;

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An Introduction to Neural Information Retrieval

provides a complete picture of neural information retrieval techniques that culminate in supervised neural learning to rank models including deep;

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Neural Networks for Natural Language Processing

highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and;

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Neural Networks for Natural Language Processing

highlighting topics including deep learning, query entity recognition, and information retrieval, this book is ideally designed for research and;

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Machine Learning for Text

intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning;

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Natural Language Processing Recipes

natural language processing techniques. Identify machine learning and deep learning techniques for natural language processing and natural;

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Representation Learning for Natural Language Processing

network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and;

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Representation Learning for Natural Language Processing

network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and;

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Deep Learning

Deep Learning provides an overview of general deep learning methodology and its applications to a variety of signal and information;

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Foundations of Statistical Natural Language Processing

comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for;

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Mining Intelligence and Knowledge Exploration

, machine learning, recommender systems, natural language processing, language, cognition and computation, and business intelligence.;

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Information Management and Big Data

issues such as data mining, artificial intelligence, Natural Language Processing, information retrieval, machine learning, web mining.;

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Mining Intelligence and Knowledge Exploration

. Accepted papers were grouped into various subtopics including information retrieval, machine learning, pattern recognition, knowledge discovery;

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Multidisciplinary Information Retrieval

research areas: information retrieval, semantic web technologies for IR, natural language processing for IR, and large-scale or distributed;

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Chinese Computational Linguistics and Natural Language Processing Based on Natur

; multilinguality in NLP; machine learning methods for NLP; knowledge graph and information extraction; discourse, coreference and pragmatics; information;

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Information Management and Big Data

. The papers address issues such as data mining, artificial intelligence, Natural Language Processing, information retrieval, machine learning;

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Web Information Systems and Applications

submissions. The papers are grouped in topical sections on machine learning and data mining, cloud computing and big data, information retrieval;

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Mining Intelligence and Knowledge Exploration

recognition, speech processing, information retrieval, natural language processing, social network analysis, security, and fuzzy rough sets.;

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Web Information Systems and Applications

103 submissions. The papers cover topics such as machine learning and data mining; cloud computing and big data; information retrieval;

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Mining Intelligence and Knowledge Exploration

consortium papers were carefully reviewed and selected from 185 submissions. The papers cover a wide range of topics including information;

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Natural Language Processing Recipes

techniques leveraging machine learning and deep learning Who This Book Is For Data scientists who want to refresh and learn various;

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Information Retrieval

models, cross-language information retrieval, peer-to-peer processing, XML search, mediators, and duplicate document detection.;

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Automatic Digital Document Processing and Management

concepts in machine learning, and providing a case study on building an intelligent system for digital document and library management; discusses;

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Clinical Text Mining

diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how;

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Statistical Language Models for Information Retrieval

to all search engines is the problem of designing an effective retrieval model that can rank documents accurately for a given query. This has;

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

and information extraction; Language resource and evaluation; Information retrieval and question answering; Text classification and;

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