Compositional Semantics
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Compositional Semantics
Author: Pauline I. Jacobson
language: en
Publisher: Oxford Textbooks in Linguistic
Release Date: 2014
This book provides an introduction to compositional semantics and to the syntax/semantics interface. It is rooted within the tradition of model theoretic semantics, and develops an explicit fragment of both the syntax and semantics of a rich portion of English. Professor Jacobson adopts a Direct Compositionality approach, whereby the syntax builds the expressions while the semantics simultaneously assigns each a model-theoretic interpretation. Alongside this approach, the author also presents a competing view that makes use of an intermediate level, Logical Form. She develops parallel treatments of a variety of phenomena from both points of view with detailed comparisons. The book begins with simple and fundamental concepts and gradually builds a more complex fragment, including analyses of more advanced topics such as focus, negative polarity, and a variety of topics centering on pronouns and binding more generally. Exercises are provided throughout, alongside open-ended questions for students to consider. The exercises are interspersed with the text to promote self-discovery of the fundamentals and their applications. The book provides a rigorous foundation in formal analysis and model theoretic semantics and is suitable for advanced undergraduate and graduate students in linguistics, philosophy of language, and related fields.
Presupposition and Implicature in Compositional Semantics
All humans can interpret sentences of their native language quickly and without effort. Working from the perspective of generative grammar, the contributors to this volume investigate three mental mechanisms, widely assumed to underlie this ability: compositional semantics, implicature computation and presupposition computation.
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 natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.