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Natural language & machine translation

See below for a selection of the latest books from Natural language & machine translation category. Presented with a red border are the Natural language & machine translation books that have been lovingly read and reviewed by the experts at Lovereading. With expert reading recommendations made by people with a passion for books and some unique features Lovereading will help you find great Natural language & machine translation books and those from many more genres to read that will keep you inspired and entertained. And it's all free!

Statistical Methods for the Study of Undeciphered Lanquages

Statistical Methods for the Study of Undeciphered Lanquages

Author: Ronojoy Adhikari Format: Hardback Release Date: 01/01/2021

This book presents an overview of the state of the art in the application of unsupervised algorithms to the study of undeciphered scripts. It shows how these algorithms can be applied to the Indus script, which is a major undeciphered script of the ancient world. The author explains how modern pattern recognition and search techniques implemented on the digital computer can considerably reduce human effort in uncovering patterns and syntax in undeciphered scripts. These can then form the basis on which semantic information and decipherment can be obtained.

Introducing Speech and Language Processing

Introducing Speech and Language Processing

Author: John (University of Oxford) Coleman Format: Paperback / softback Release Date: 02/11/2020

This major new textbook provides a clearly-written, concise and accessible introduction to speech and language processing. Assuming knowledge of only the very basics of linguistics and written specifically for students with no technical background, it is the perfect starting point for anyone beginning to study the discipline. Students are introduced to topics such as digital signal processing, speech analysis and synthesis, finite-state machines, automatic speech recognition, parsing and probabilistic grammars, and are shown from a very elementary level how to work with two programming languages, C and Prolog. The accompanying CD-ROM contains all the software described in the book, along with a C compiler, Prolog interpreter and sound file editor, thus providing a self-contained, one-stop resource for the learner. Setting a firm grounding in speech and language processing and an invaluable foundation for further study, Introducing Speech and Language Processing is set to become the leading introduction to the field.

Automatic Disambiguation of Author Names in Bibliographic Repositories

Automatic Disambiguation of Author Names in Bibliographic Repositories

Author: Anderson A. Ferreira, Marcos Andre Goncalves, Alberto H. F. Laender Format: Hardback Release Date: 30/09/2020

This book deals with a hard problem that is inherent to human language: ambiguity. In particular, we focus on author name ambiguity, a type of ambiguity that exists in digital bibliographic repositories, which occurs when an author publishes works under distinct names or distinct authors publish works under similar names. This problem may be caused by a number of reasons, including the lack of standards and common practices, and the decentralized generation of bibliographic content. As a consequence, the quality of the main services of digital bibliographic repositories such as search, browsing, and recommendation may be severely affected by author name ambiguity. The focal point of the book is on automatic methods, since manual solutions do not scale to the size of the current repositories or the speed in which they are updated. Accordingly, we provide an ample view on the problem of automatic disambiguation of author names, summarizing the results of more than a decade of research on this topic conducted by our group, which were reported in more than a dozen publications that received over 900 citations so far, according to Google Scholar. We start by discussing its motivational issues (Chapter 1). Next, we formally define the author name disambiguation task (Chapter 2) and use this formalization to provide a brief, taxonomically organized, overview of the literature on the topic (Chapter 3). We then organize, summarize and integrate the efforts of our own group on developing solutions for the problem that have historically produced state-of-the-art (by the time of their proposals) results in terms of the quality of the disambiguation results. Thus, Chapter 4 covers HHC - Heuristic-based Clustering, an author name disambiguation method that is based on two specific real-world assumptions regarding scientific authorship. Then, Chapter 5 describes SAND - Self-training Author Name Disambiguator and Chapter 6 presents two incremental author name disambiguation methods, namely INDi - Incremental Unsupervised Name Disambiguation and INC- Incremental Nearest Cluster. Finally, Chapter 7 provides an overview of recent author name disambiguation methods that address new specific approaches such as graph-based representations, alternative predefined similarity functions, visualization facilities and approaches based on artificial neural networks. The chapters are followed by three appendices that cover, respectively: (i) a pattern matching function for comparing proper names and used by some of the methods addressed in this book; (ii) a tool for generating synthetic collections of citation records for distinct experimental tasks; and (iii) a number of datasets commonly used to evaluate author name disambiguation methods. In summary, the book organizes a large body of knowledge and work in the area of author name disambiguation in the last decade, hoping to consolidate a solid basis for future developments in the field.

Automatic Disambiguation of Author Names in Bibliographic Repositories

Automatic Disambiguation of Author Names in Bibliographic Repositories

Author: Anderson A. Ferreira, Marcos Andre Goncalves, Alberto H. F. Laender Format: Paperback / softback Release Date: 30/09/2020

This book deals with a hard problem that is inherent to human language: ambiguity. In particular, we focus on author name ambiguity, a type of ambiguity that exists in digital bibliographic repositories, which occurs when an author publishes works under distinct names or distinct authors publish works under similar names. This problem may be caused by a number of reasons, including the lack of standards and common practices, and the decentralized generation of bibliographic content. As a consequence, the quality of the main services of digital bibliographic repositories such as search, browsing, and recommendation may be severely affected by author name ambiguity. The focal point of the book is on automatic methods, since manual solutions do not scale to the size of the current repositories or the speed in which they are updated. Accordingly, we provide an ample view on the problem of automatic disambiguation of author names, summarizing the results of more than a decade of research on this topic conducted by our group, which were reported in more than a dozen publications that received over 900 citations so far, according to Google Scholar. We start by discussing its motivational issues (Chapter 1). Next, we formally define the author name disambiguation task (Chapter 2) and use this formalization to provide a brief, taxonomically organized, overview of the literature on the topic (Chapter 3). We then organize, summarize and integrate the efforts of our own group on developing solutions for the problem that have historically produced state-of-the-art (by the time of their proposals) results in terms of the quality of the disambiguation results. Thus, Chapter 4 covers HHC - Heuristic-based Clustering, an author name disambiguation method that is based on two specific real-world assumptions regarding scientific authorship. Then, Chapter 5 describes SAND - Self-training Author Name Disambiguator and Chapter 6 presents two incremental author name disambiguation methods, namely INDi - Incremental Unsupervised Name Disambiguation and INC- Incremental Nearest Cluster. Finally, Chapter 7 provides an overview of recent author name disambiguation methods that address new specific approaches such as graph-based representations, alternative predefined similarity functions, visualization facilities and approaches based on artificial neural networks. The chapters are followed by three appendices that cover, respectively: (i) a pattern matching function for comparing proper names and used by some of the methods addressed in this book; (ii) a tool for generating synthetic collections of citation records for distinct experimental tasks; and (iii) a number of datasets commonly used to evaluate author name disambiguation methods. In summary, the book organizes a large body of knowledge and work in the area of author name disambiguation in the last decade, hoping to consolidate a solid basis for future developments in the field.

Natural Language Processing

Natural Language Processing

Author: Ela Kumar Format: Paperback / softback Release Date: 26/09/2020

Natural Language Processing 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. The book broadly deals with: The basic area of natural language processing, its significance and applications, its history, role of knowledge in language processing, practical language processing systems. Various techniques for performing syntactic analysis; phonological analysis, morphological analysis, word level analysis and other related topics; the concept of ambiguity and techniques to resolve ambiguities in language processing. Techniques used for understanding the meaning of sentences. The grammatical formalisms that are developed to accommodate word meaning and collective sentence meaning. Elementary and advanced parsing techniques. Augmented and feature grammars. The advanced grammar formalisms used for capturing meaning of sentences. Use of knowledge in language processing. Knowledge representation techniques. First order predicate calculus and different inference mechanisms that are used to draw conclusions from sentences. Concept of anaphora, pragmatic and discourse understanding theories. Various issues of Natural Language generation. It provides elaborate examples and gives much emphasis to the computational systems related to different aspects of Natural Language Processing. The book will be useful for the B.Tech. and M.Tech. students.

Semantic Modeling for Data

Semantic Modeling for Data

Author: Panos Alexopoulos Format: Paperback / softback Release Date: 04/09/2020

What value does semantic data modeling offer? As an information architect or data science professional, let's say you have an abundance of the right data and the technology to extract business gold-but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You'll learn how to master this craft to increase the usability and value of your data and applications. You'll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data. Understand the fundamental concepts, phenomena, and processes related to semantic data modeling Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and tools Avoid mistakes and bad practices that can undermine your efforts to create good data models Learn about model development dilemmas, including representation, expressiveness and content, development, and governance Organize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges

Natural Language Processing with SAS

Natural Language Processing with SAS

Author: Katie Tedrow Format: Paperback / softback Release Date: 31/08/2020

Experimental IR Meets Multilinguality, Multimodality, and Interaction

Experimental IR Meets Multilinguality, Multimodality, and Interaction

Author: Avi Arampatzis Format: Paperback / softback Release Date: 28/08/2020

This book constitutes the refereed proceedings of the 11th International Conference of the CLEF Association, CLEF 2020, held in Thessaloniki, Greece, in September 2020.*The conference has a clear focus on experimental information retrieval with special attention to the challenges of multimodality, multilinguality, and interactive search ranging from unstructured to semi structures and structured data. The 5 full papers and 2 short papers presented in this volume were carefully reviewed and selected from 9 submissions. This year, the contributions addressed the following challenges: a large-scale evaluation of translation effects in academic search, advancement of assessor-driven aggregation methods for efficient relevance assessments, and development of a new test dataset. In addition to this, the volume presents 7 best of the labs papers which were reviewed as full paper submissions with the same review criteria. The 12 lab overview papers were accepted out of 15 submissions and represent scientific challenges based on new data sets and real world problems in multimodal and multilingual information access. * The conference was held virtually due to the COVID-19 pandemic.

Natural Language Processing for Global and Local Business

Natural Language Processing for Global and Local Business

Author: Fatih Pinarbasi Format: Paperback / softback Release Date: 24/07/2020

Natural Language Processing for Global and Local Business

Natural Language Processing for Global and Local Business

Author: Fatih Pinarbasi Format: Hardback Release Date: 24/07/2020