This book covers topics in formal linguistics, intonational phonology, computational linguistics, and experimental psycholinguistics, presenting them as an integrated theory of the language faculty. In this book Mark Steedman argues that the surface syntax of natural languages maps spoken and written forms directly to a compositional semantic representation that includes predicate-argument structure, quantification, and information structure without constructing any intervening structural representation. His purpose is to construct a principled theory of natural grammar that is directly compatible with both explanatory linguistic accounts of a number of problematic syntactic phenomena and a straightforward computational account of the way sentences are mapped onto representations of meaning. The radical nature of Steedman's proposal stems from his claim that much of the apparent complexity of syntax, prosody, and processing follows from the lexical specification of the grammar and from the involvement of a small number of universal rule-types for combining predicates and arguments. These syntactic operations are related to the combinators of Combinatory Logic, engendering a much freer definition of derivational constituency than is traditionally assumed. This property allows Combinatory Categorial Grammar to capture elegantly the structure and interpretation of coordination and intonation contour in English as well as some well-known interactions between word order, coordination, and relativization across a number of other languages. It also allows more direct compatibility with incremental semantic interpretation during parsing. The book covers topics in formal linguistics, intonational phonology, computational linguistics, and experimental psycholinguistics, presenting them as an integrated theory of the language faculty in a form accessible to readers from any of those fields.

This book provides an overview of the structures, topics and main theories of syntactic processing. It covers the last 40 years of sentence-level psycholinguistic research and debates and makes it accessible to both theoretical linguists and experimental psychologists. Tying linguistically relevant issues to psycholinguistic theory, this book: Covers the processing of the grammatical phenomena adjunction, agreement and gap filling and discusses the relationship between grammars and parsers Discusses experimental work and theories, demonstrating how psychologists have made real strides in understanding language and how studying the processing of syntactic structure is the same as studying the nature of language Explores the key theories of psycholinguistics, including recent developments Explains the different methodologies of sentence processing, such as eye-tracking and electroencephalography Bridging the gap between psycholinguistic research and the study of language, this book is essential reading for advanced students and scholars of linguistics and experimental psycholinguistics as well as cognitive science and psychology.

An Introduction to Syntactic Analysis and Theory offers beginning students a comprehensive overview of and introduction to our current understanding of the rules and principles that govern the syntax of natural languages. Includes numerous pedagogical features such as 'practice' boxes and sidebars, designed to facilitate understanding of both the 'hows' and the 'whys' of sentence structure Guides readers through syntactic and morphological structures in a progressive manner Takes the mystery out of one of the most crucial aspects of the workings of language – the principles and processes behind the structure of sentences Ideal for students with minimal knowledge of current syntactic research, it progresses in theoretical difficulty from basic ideas and theories to more complex and advanced, up to date concepts in syntactic theory

Studies on the syntactic consequences of event type in languages have shown that "Aktionsart" plays a role in Universal Grammar. This book contributes to the exploration of the syntax/semantics interface by presenting a thorough comparison of event and predicate types in English and Spanish. The mapping between event and syntactic predicate types, including detransitives, is given a minimalist account based on the functional categories that embed event features and on a careful analysis of the features checked by objects. As the book delves into the theoretical issue of how parameters are characterized, it presents the most comprehensive account to date of event type phenomena in Spanish, an innovative analysis of the clitic "SE" and a re-definition of unaccusativity. The theory is then applied to the ongoing issues in the sentence processing literature. A proposal is made for an update of the current data in light of these latest linguistic discoveries.

This volume deals with research on the processing of a native language, second language learning, bilingualism, typical and impaired syntax processing. The articles presented here cover a number of linguistic phenomena, including passives, temporal concord, object pronouns, reflexives, embedded sentences, relative clauses, wh-movement, and binding theory. They also apply various experimental methods, such as eye tracking, reaction times, event-related potentials, picture selection tasks, sentence elicitation, pupillometry, and picture matching tasks. As such, this book details a number of the most representative methods used in language processing.

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Leverage the power of machine learning and deep learning to extract information from text data About This Book Implement Machine Learning and Deep Learning techniques for efficient natural language processing Get started with NLTK and implement NLP in your applications with ease Understand and interpret human languages with the power of text analysis via Python Who This Book Is For This book is intended for Python developers who wish to start with natural language processing and want to make their applications smarter by implementing NLP in them. What You Will Learn Focus on Python programming paradigms, which are used to develop NLP applications Understand corpus analysis and different types of data attribute. Learn NLP using Python libraries such as NLTK, Polyglot, SpaCy, Standford CoreNLP and so on Learn about Features Extraction and Feature selection as part of Features Engineering. Explore the advantages of vectorization in Deep Learning. Get a better understanding of the architecture of a rule-based system. Optimize and fine-tune Supervised and Unsupervised Machine Learning algorithms for NLP problems. Identify Deep Learning techniques for Natural Language Processing and Natural Language Generation problems. In Detail This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them. During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis. You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data. By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world. Style and approach This book teaches the readers various aspects of natural language Processing using NLTK. It takes the reader from the basic to advance level in a smooth way.