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Representation and Inference for Natural Language

computing semantic representations for fragments of natural language. This volume will be an essential text for computer scientists, linguists, and;

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Natural Language Processing

of Natural Language Processing. The book will be useful for the B.Tech. and M.Tech. students.;

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Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for;

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Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for;

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Natural Language Processing & Knowledge Representation - Language for Knowledge & Knowledge for Language (OI)

of representation and inference in NL makes it the ideal level for all tasks in an intelligent computer system. NL processing combines the qualitative;

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Linguistic Issues in Language Technology Vol 9 - Perspectives on Semantic Representations for Textual Inference

in both existing and newly emerging areas of computational language analysis. The vast quantity of electronically accessible natural language data;

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Coarse-to-Fine Natural Language Processing

plausibility of grammatical constructions. This book develops a general coarse-to-fine framework for learning and inference in large statistical;

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Plan Recognition In Natural Language Dialogue

In most current natural language systems each query is treated as an isolated request for information regardless of its context in dialogue;

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Grammatical Inference: Learning Syntax from Sentences: Third International Colloquium, Icgi-96, Montpellier, France, September 25 - 27, 1996. Proceedi

carefully selected for presentation at the conference. The papers are organized in sections on algebraic methods and algorithms, natural language;

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Bayesian Analysis in Natural Language Processing

Natural language processing (NLP) went through a profound transformation in the mid-1980s when it shifted to make heavy use of corpora and;

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Linguistic Structure Prediction

basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to;

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Expressibility And The Problem Of Efficient Text Planning

of representation called the Text Structure, an intermediate between the representation of world and the language.;

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Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications

, Probability, Inference and Prediction Methods, Random Processes, Bayesian Methods, Machine Learning, Artificial Neural Networks for Natural Language;

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Discovery And Fusion Of Uncertain Knowledge In Data

Data analysis is of upmost importance in the mining of big data, where knowledge discovery and inference are the basis for intelligent;

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Foundations of Intensional Semantics

semantics, and the formal power of the semantic representation language. It proposes a theory that offers a promising framework for developing a;

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Deep Learning and Linguistic Representation

achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the;

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Deep Learning and Linguistic Representation

achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the;

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Meaning Without Representation

that therefore the notion of representation should play a fundamental explanatory role in any explanation of language and language use. Leading;

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Resource-Sensitivity, Binding and Anaphora

categorial framework offers a natural setting in which to study questions of grammatical composition, both empirically and abstractly. There are;

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Controlled Natural Language

selected from 17 initial submissions. They broadly cover the field of controlled natural language, stressing theoretical and practical aspects;

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Sentic Computing

In this book common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural;

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Linguistic Structure Prediction

basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to;

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Logic & Natural Language

, and that as a result the calculus is inadequate for the analysis of the semantics and logic of natural language. Ben-Yami develops both an;

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Mathematical Aspects Of Natural And Formal Languages

mathematically oriented, practical issues such as cryptography, grammatical inference and natural language processing are also discussed.;

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Computing Meaning

in further specification of these representations. Underspecified representation and inference are therefore the key topics in this book. The;

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Cambridge Textbooks in Linguistics

developed within formal logic for the study of artificial languages have for some time been fruitfully applied to the study of natural languages and;

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Studies in Natural Language Processing

survey of research in relational semantics, but offers a stimulus for potential research advances in semantics, natural language processing and;

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