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Language Processing In Discourse Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Language Processing In Discourse book. This book definitely worth reading, it is an incredibly well-written.
Author : Dan Jurafsky
Publisher : Pearson Education India
Page : 912 pages
File Size : 45,8 Mb
Release : 2000-09
Category : Electronic
ISBN : 8131716724
Author : Monika Doherty
Publisher : Routledge
Page : 321 pages
File Size : 53,9 Mb
Release : 2003-09-02
Category : Language Arts & Disciplines
ISBN : 9781134460564
This book argues that language systems determine language use to a greater extent than is generally assumed. The author demonstrates how the typological characteristics of a language determine even the most general aspects of our stylistic preferences. Through extensive analysis of examples in German and English, the author demonstrates how analogous options of sentence structure must be surrendered in order to achieve felicitous translations. Two major aspects that determine the appropriateness of language use are examined: language processing and discourse-dependency. Essential reading for translation scholars and linguists involved in the comparative study of English and German, this book will also be of interest to scholars of psycholinguistics and cognitive science, as well as translators and linguists more generally.
Author : Manfred Stede
Publisher : Morgan & Claypool Publishers
Page : 168 pages
File Size : 50,8 Mb
Release : 2012
Category : Computers
ISBN : 9781608457342
Discourse Processing here is framed as marking up a text with structural descriptions on several levels, which can serve to support many language-processing or text-mining tasks. We first explore some ways of assigning structure on the document level: the logical document structure as determined by the layout of the text, its genre-specific content structure, and its breakdown into topical segments. Then the focus moves to phenomena of local coherence. We introduce the problem of coreference and look at methods for building chains of coreferring entities in the text. Next, the notion of coherence relation is introduced as the second important factor of local coherence. We study the role of connectives and other means of signaling such relations in text, and then return to the level of larger textual units, where tree or graph structures can be ascribed by recursively assigning coherence relations. Taken together, these descriptions can inform text summarization, information extraction, discourse-aware sentiment analysis, question answering, and the like. Table of Contents: Introduction / Large Discourse Units and Topics / Coreference Resolution / Small Discourse Units and Coherence Relations / Summary: Text Structure on Multiple Interacting Levels
Author : Nitin Hardeniya,Jacob Perkins,Deepti Chopra,Nisheeth Joshi,Iti Mathur
Publisher : Packt Publishing Ltd
Page : 687 pages
File Size : 50,9 Mb
Release : 2016-11-22
Category : Computers
ISBN : 9781787287846
Learn to build expert NLP and machine learning projects using NLTK and other Python libraries About This Book Break text down into its component parts for spelling correction, feature extraction, and phrase transformation Work through NLP concepts with simple and easy-to-follow programming recipes Gain insights into the current and budding research topics of NLP Who This Book Is For If you are an NLP or machine learning enthusiast and an intermediate Python programmer who wants to quickly master NLTK for natural language processing, then this Learning Path will do you a lot of good. Students of linguistics and semantic/sentiment analysis professionals will find it invaluable. What You Will Learn The scope of natural language complexity and how they are processed by machines Clean and wrangle text using tokenization and chunking to help you process data better Tokenize text into sentences and sentences into words Classify text and perform sentiment analysis Implement string matching algorithms and normalization techniques Understand and implement the concepts of information retrieval and text summarization Find out how to implement various NLP tasks in Python In Detail Natural Language Processing is a field of computational linguistics and artificial intelligence that deals with human-computer interaction. It provides a seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning. The number of human-computer interaction instances are increasing so it's becoming imperative that computers comprehend all major natural languages. The first NLTK Essentials module is an introduction on how to build systems around NLP, with a focus on how to create a customized tokenizer and parser from scratch. You will learn essential concepts of NLP, be given practical insight into open source tool and libraries available in Python, shown how to analyze social media sites, and be given tools to deal with large scale text. This module also provides a workaround using some of the amazing capabilities of Python libraries such as NLTK, scikit-learn, pandas, and NumPy. The second Python 3 Text Processing with NLTK 3 Cookbook module teaches you the essential techniques of text and language processing with simple, straightforward examples. This includes organizing text corpora, creating your own custom corpus, text classification with a focus on sentiment analysis, and distributed text processing methods. The third Mastering Natural Language Processing with Python module will help you become an expert and assist you in creating your own NLP projects using NLTK. You will be guided through model development with machine learning tools, shown how to create training data, and given insight into the best practices for designing and building NLP-based applications using Python. This Learning Path combines some of the best that Packt has to offer in one complete, curated package and is designed to help you quickly learn text processing with Python and NLTK. It includes content from the following Packt products: NTLK essentials by Nitin Hardeniya Python 3 Text Processing with NLTK 3 Cookbook by Jacob Perkins Mastering Natural Language Processing with Python by Deepti Chopra, Nisheeth Joshi, and Iti Mathur Style and approach This comprehensive course creates a smooth learning path that teaches you how to get started with Natural Language Processing using Python and NLTK. You'll learn to create effective NLP and machine learning projects using Python and NLTK.
Author : Dafydd Gibbon
Publisher : Walter de Gruyter GmbH & Co KG
Page : 416 pages
File Size : 54,6 Mb
Release : 2016-09-26
Category : Language Arts & Disciplines
ISBN : 9783110821895
Author : Mihai Dascălu
Publisher : Springer
Page : 279 pages
File Size : 44,9 Mb
Release : 2013-11-26
Category : Technology & Engineering
ISBN : 9783319034195
With the advent and increasing popularity of Computer Supported Collaborative Learning (CSCL) and e-learning technologies, the need of automatic assessment and of teacher/tutor support for the two tightly intertwined activities of comprehension of reading materials and of collaboration among peers has grown significantly. In this context, a polyphonic model of discourse derived from Bakhtin’s work as a paradigm is used for analyzing both general texts and CSCL conversations in a unique framework focused on different facets of textual cohesion. As specificity of our analysis, the individual learning perspective is focused on the identification of reading strategies and on providing a multi-dimensional textual complexity model, whereas the collaborative learning dimension is centered on the evaluation of participants’ involvement, as well as on collaboration assessment. Our approach based on advanced Natural Language Processing techniques provides a qualitative estimation of the learning process and enhances understanding as a “mediator of learning” by providing automated feedback to both learners and teachers or tutors. The main benefits are its flexibility, extensibility and nevertheless specificity for covering multiple stages, starting from reading classroom materials, to discussing on specific topics in a collaborative manner and finishing the feedback loop by verbalizing metacognitive thoughts.
Author : Ankur A. Patel,Ajay Uppili Arasanipalai
Publisher : "O'Reilly Media, Inc."
Page : 336 pages
File Size : 41,7 Mb
Release : 2021-05-12
Category : Computers
ISBN : 9781492062547
NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, you'll learn how to build, train, and deploy models for real-world applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about Transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Build core parts of the NLP pipeline--including tokenizers, embeddings, and language models--from scratch using Python and PyTorch Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production
Author : Alexander Clark,Chris Fox,Shalom Lappin
Publisher : John Wiley & Sons
Page : 802 pages
File Size : 48,5 Mb
Release : 2013-04-24
Category : Language Arts & Disciplines
ISBN : 9781118448670
This comprehensive reference work provides an overview of the concepts, methodologies, and applications in computational linguistics and natural language processing (NLP). Features contributions by the top researchers in the field, reflecting the work that is driving the discipline forward Includes an introduction to the major theoretical issues in these fields, as well as the central engineering applications that the work has produced Presents the major developments in an accessible way, explaining the close connection between scientific understanding of the computational properties of natural language and the creation of effective language technologies Serves as an invaluable state-of-the-art reference source for computational linguists and software engineers developing NLP applications in industrial research and development labs of software companies
Author : Dafydd Gibbon
Publisher : Walter de Gruyter
Page : 1278 pages
File Size : 42,9 Mb
Release : 1996
Category : Computers
ISBN : 3110154498
Author : Jalaj Thanaki
Publisher : Packt Publishing Ltd
Page : 486 pages
File Size : 45,6 Mb
Release : 2017-07-31
Category : Computers
ISBN : 9781787285521
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.
Author : Dan Jurafsky,James H. Martin
Publisher : Prentice Hall
Page : 1027 pages
File Size : 43,8 Mb
Release : 2009
Category : Automatic speech recognition
ISBN : 9780131873216
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora. Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.
Author : Monika Doherty
Publisher : Unknown
Page : 0 pages
File Size : 54,9 Mb
Release : 2002
Category : Electronic
ISBN : OCLC:863207345
Author : Ela Kumar
Publisher : I. K. International Pvt Ltd
Page : 220 pages
File Size : 54,6 Mb
Release : 2013-12-30
Category : Computers
ISBN : 9789380578774
Covers all aspects of the area of linguistic analysis and the computational systems that have been developed to perform the language analysis. The book is primarily meant for post graduate and undergraduate technical courses.
Author : Mohamed Zakaria Kurdi
Publisher : John Wiley & Sons
Page : 323 pages
File Size : 46,5 Mb
Release : 2018-02-28
Category : Technology & Engineering
ISBN : 9781848219212
Natural Language Processing (NLP) is a scientific discipline which is found at the intersection of fields such as Artificial Intelligence, Linguistics, and Cognitive Psychology. This book presents in four chapters the state of the art and fundamental concepts of key NLP areas. Are presented in the first chapter the fundamental concepts in lexical semantics, lexical databases, knowledge representation paradigms, and ontologies. The second chapter is about combinatorial and formal semantics. Discourse and text representation as well as automatic discourse segmentation and interpretation, and anaphora resolution are the subject of the third chapter. Finally, in the fourth chapter, I will cover some aspects of large scale applications of NLP such as software architecture and their relations to cognitive models of NLP as well as the evaluation paradigms of NLP software. Furthermore, I will present in this chapter the main NLP applications such as Machine Translation (MT), Information Retrieval (IR), as well as Big Data and Information Extraction such as event extraction, sentiment analysis and opinion mining.
Author : A. Flammer,W. Kinzelbach
Publisher : Elsevier
Page : 625 pages
File Size : 51,6 Mb
Release : 2000-04-01
Category : Psychology
ISBN : 9780080866628
Research on discourse (or text) processing has only recently come into its own. It builds on the work of text analysis which has a long and distinguished history, but modern developments in psychology (e.g. memory research), artificial intelligence, linguistics and philosophy have contributed to this emergence in the last decade as a lively and promising research area. This book contains 46 selected and edited contributions from the International Symposium held in Fribourg in 1981, and represents a truly international overview of the developments in research on written and oral discourse. The contributions have been grouped according to problem area and not according to methodology, with the intention of focusing on the important issues in the field of discourse processing and of showing how diverse approaches contribute to a better understanding of the problems involved. The main themes are: text structure, coherence, inference, memory processes, attention and control, goal perspectives, and educational implications.