Building Natural Language Generation Systems
Building Natural Language Generation Systems

Author: Ehud Reiter

Publisher: Cambridge University Press

Published: 2000-01-28

Total Pages: 274

ISBN-13: 0521620368

DOWNLOAD EBOOK

This book explains how to build Natural Language Generation (NLG) systems - computer software systems which use techniques from artificial intelligence and computational linguistics to automatically generate understandable texts in English or other human languages, either in isolation or as part of multimedia documents, Web pages, and speech output systems. Typically starting from some non-linguistic representation of information as input, NLG systems use knowledge about language and the application domain to automatically produce documents, reports, explanations, help messages, and other kinds of texts. The book covers the algorithms and representations needed to perform the core tasks of document planning, microplanning, and surface realization, using a case study to show how these components fit together. It also discusses engineering issues such as system architecture, requirements analysis, and the integration of text generation into multimedia and speech output systems.

Natural Language Generation in Artificial Intelligence and Computational Linguistics
Natural Language Generation in Artificial Intelligence and Computational Linguistics

Author: Cecile L. Paris

Publisher: Springer Science & Business Media

Published: 2013-03-14

Total Pages: 414

ISBN-13: 1475759452

DOWNLOAD EBOOK

One of the aims of Natural Language Processing is to facilitate .the use of computers by allowing their users to communicate in natural language. There are two important aspects to person-machine communication: understanding and generating. While natural language understanding has been a major focus of research, natural language generation is a relatively new and increasingly active field of research. This book presents an overview of the state of the art in natural language generation, describing both new results and directions for new research. The principal emphasis of natural language generation is not only to facili tate the use of computers but also to develop a computational theory of human language ability. In doing so, it is a tool for extending, clarifying and verifying theories that have been put forth in linguistics, psychology and sociology about how people communicate. A natural language generator will typically have access to a large body of knowledge from which to select information to present to users as well as numer of expressing it. Generating a text can thus be seen as a problem of ous ways decision-making under multiple constraints: constraints from the propositional knowledge at hand, from the linguistic tools available, from the communicative goals and intentions to be achieved, from the audience the text is aimed at and from the situation and past discourse. Researchers in generation try to identify the factors involved in this process and determine how best to represent the factors and their dependencies.

Natural Language Generation in Interactive Systems
Natural Language Generation in Interactive Systems

Author: Amanda Stent

Publisher: Cambridge University Press

Published: 2014-06-12

Total Pages: 383

ISBN-13: 1107010020

DOWNLOAD EBOOK

A comprehensive overview of the state-of-the-art in natural language generation for interactive systems, with links to resources for further research.

Computational Linguistics and Intelligent Text Processing
Computational Linguistics and Intelligent Text Processing

Author: Alexander Gelbukh

Publisher: Springer

Published: 2009-02-17

Total Pages: 619

ISBN-13: 3642003826

DOWNLOAD EBOOK

th CICLing 2009 markedthe 10 anniversary of the Annual Conference on Intel- gent Text Processing and Computational Linguistics. The CICLing conferences provide a wide-scope forum for the discussion of the art and craft of natural language processing research as well as the best practices in its applications. This volume contains ?ve invited papers and the regular papers accepted for oral presentation at the conference. The papers accepted for poster presentation were published in a special issue of another journal (see the website for more information). Since 2001, the proceedings of CICLing conferences have been published in Springer’s Lecture Notes in Computer Science series, as volumes 2004, 2276, 2588, 2945, 3406, 3878, 4394, and 4919. This volume has been structured into 12 sections: – Trends and Opportunities – Linguistic Knowledge Representation Formalisms – Corpus Analysis and Lexical Resources – Extraction of Lexical Knowledge – Morphology and Parsing – Semantics – Word Sense Disambiguation – Machine Translation and Multilinguism – Information Extraction and Text Mining – Information Retrieval and Text Comparison – Text Summarization – Applications to the Humanities A total of 167 papers by 392 authors from 40 countries were submitted for evaluation by the International Program Committee, see Tables 1 and 2. This volume contains revised versions of 44 papers, by 120 authors, selected for oral presentation; the acceptance rate was 26. 3%.

Natural Language Processing
Natural Language Processing

Author: Yue Zhang

Publisher: Cambridge University Press

Published: 2021-01-07

Total Pages: 487

ISBN-13: 1108420214

DOWNLOAD EBOOK

This undergraduate textbook introduces essential machine learning concepts in NLP in a unified and gentle mathematical framework.

Natural Language Processing in Artificial Intelligence
Natural Language Processing in Artificial Intelligence

Author: Brojo Kishore Mishra

Publisher: CRC Press

Published: 2020-11-01

Total Pages: 297

ISBN-13: 1000711315

DOWNLOAD EBOOK

This volume focuses on natural language processing, artificial intelligence, and allied areas. Natural language processing enables communication between people and computers and automatic translation to facilitate easy interaction with others around the world. This book discusses theoretical work and advanced applications, approaches, and techniques for computational models of information and how it is presented by language (artificial, human, or natural) in other ways. It looks at intelligent natural language processing and related models of thought, mental states, reasoning, and other cognitive processes. It explores the difficult problems and challenges related to partiality, underspecification, and context-dependency, which are signature features of information in nature and natural languages. Key features: Addresses the functional frameworks and workflow that are trending in NLP and AI Looks at the latest technologies and the major challenges, issues, and advances in NLP and AI Explores an intelligent field monitoring and automated system through AI with NLP and its implications for the real world Discusses data acquisition and presents a real-time case study with illustrations related to data-intensive technologies in AI and NLP.

Deep Learning in Natural Language Processing
Deep Learning in Natural Language Processing

Author: Li Deng

Publisher: Springer

Published: 2018-05-23

Total Pages: 338

ISBN-13: 9811052093

DOWNLOAD EBOOK

In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.

Introduction to Natural Language Processing
Introduction to Natural Language Processing

Author: Jacob Eisenstein

Publisher: MIT Press

Published: 2019-10-01

Total Pages: 535

ISBN-13: 0262042843

DOWNLOAD EBOOK

A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learning techniques. This textbook provides a technical perspective on natural language processing—methods for building computer software that understands, generates, and manipulates human language. It emphasizes contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. The first section establishes a foundation in machine learning by building a set of tools that will be used throughout the book and applying them to word-based textual analysis. The second section introduces structured representations of language, including sequences, trees, and graphs. The third section explores different approaches to the representation and analysis of linguistic meaning, ranging from formal logic to neural word embeddings. The final section offers chapter-length treatments of three transformative applications of natural language processing: information extraction, machine translation, and text generation. End-of-chapter exercises include both paper-and-pencil analysis and software implementation. The text synthesizes and distills a broad and diverse research literature, linking contemporary machine learning techniques with the field's linguistic and computational foundations. It is suitable for use in advanced undergraduate and graduate-level courses and as a reference for software engineers and data scientists. Readers should have a background in computer programming and college-level mathematics. After mastering the material presented, students will have the technical skill to build and analyze novel natural language processing systems and to understand the latest research in the field.

Embeddings in Natural Language Processing
Embeddings in Natural Language Processing

Author: Mohammad Taher Pilehvar

Publisher: Morgan & Claypool Publishers

Published: 2020-11-13

Total Pages: 177

ISBN-13: 1636390226

DOWNLOAD EBOOK

Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.