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.

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

Highly readable and eminently practical, Syntactic Analysis: The Basics focuses on bringing students with little background in linguistics up to speed on how modern syntactic analysis works. A succinct and practical introduction to understanding sentence structure, ideal for students who need to get up to speed on key concepts in the field Introduces readers to the central terms and concepts in syntax Offers a hands-on approach to understanding and performing syntactic analysis and introduces students to linguistic argumentation Includes numerous problem sets, helpfully graded for difficulty, with model answers provided at critical points Prepares readers for more advanced work with syntactic systems and syntactic analyses

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Analyzing Syntax: A Lexical-Functional Approach is a comprehensive and accessible 2004 textbook on syntactic analysis, designed for students of linguistics at advanced undergraduate or graduate level. Working within the 'Lexical Functional Grammar' (LFG) approach, it provides students with a framework for analyzing and describing grammatical structure, using extensive examples from both European and non-European languages. Topics covered include: tests for constituency, passivization and other relation-changing processes, reflexive pronouns, the control relation, Topic and Focus, relative clauses and Wh-questions, causative constructions, serial verbs, 'quirky case', and ergativity. As well as building on what linguists have learned about language in general, particular attention is paid to the unique features of individual languages. While its primary focus is on syntactic structure, the book also deals with aspects of meaning, function and word-structure that are directly relevant to syntax. Clearly organised into topics, this textbook is ideal for one-semester courses in syntax and grammatical analysis.

This book is designed to teach undergraduate and beginning graduate students how to understand, analyse and describe syntactic phenomena in different languages. The book covers every aspect of syntax from the basics to more specialised topics, such as clitics which have grammatical importance but cannot be used in isolation, and negation, in which a construction contradicts the meaning of a sentence. The approach taken combines concepts from different theoretical schools, which view syntax differently. These include M. A. K. Halliday's systemic functional linguistics, the stratificational school advocated by Sydney Lamb, and Kenneth L. Pike's tagmemic model. The emphasis of the book is on syntactic structures rather than linguistic meaning, and the book stresses the difference between a well-formed sentence and a meaningful one. The final chapter brings these two aspects together, to show the connections between syntax and semology. Each chapter concludes with exercises from a diverse range of languages and a list of major technical terms. The book also includes a glossary as an essential resource for students approaching this difficult subject for the first time.

Aus dem Inhalt: Preliminary remarks about syntactic analysis · The syntactic framework · Word classes · Phrases · Clauses · Valency · The meaning of sentences · An analytical framework

This comprehensive introduction to syntax explains the basic concepts of syntax, and how the structures which are in place for describing the world can also be applied to a description of language structure. Edith Moravcsik presents a detailed introduction to syntactic description, including linear order, selection, categories, meaning, sound form, variation and change. The final selection provides a summary which looks at how we can explain syntax. The book includes student-friendly features, such as chapter summaries, suggestions for further reading, exercises, and a glossary of terms.

In The Essence of Linguistic Analysis by R. M. W. Dixon relates together, in a clear and succinct manner, individual grammatical categories, showing their dependencies and locating each in its place within the overall tapestry of a language.

This is the first book to bring together the fields of theoretical and empirical studies in syntax on the one hand and the methodology of quantitative linguistics on the other hand. An introduction into the aims and methods of the quantitative approach to linguistics in general and to syntax in particular prepares the reader for the following chapters, which inform about measurement and data acquisition methods and the most common mathematical models for the analysis of syntactic and syntagmatic material. Various examples illustrate how these models are applied and show the corresponding results.