Graph Based Natural Language Processing And Information Retrieval
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Author |
: Rada Mihalcea |
Publisher |
: Cambridge University Press |
Total Pages |
: 201 |
Release |
: 2011-04-11 |
ISBN-10 |
: 9781139498821 |
ISBN-13 |
: 1139498827 |
Rating |
: 4/5 (21 Downloads) |
Synopsis Graph-based Natural Language Processing and Information Retrieval by : Rada Mihalcea
Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.
Author |
: Rada Mihalcea |
Publisher |
: Cambridge University Press |
Total Pages |
: 202 |
Release |
: 2011-04-11 |
ISBN-10 |
: 0521896134 |
ISBN-13 |
: 9780521896139 |
Rating |
: 4/5 (34 Downloads) |
Synopsis Graph-based Natural Language Processing and Information Retrieval by : Rada Mihalcea
Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications, and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification, and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.
Author |
: Rada Mihalcea |
Publisher |
: |
Total Pages |
: 192 |
Release |
: 2011 |
ISBN-10 |
: 1139069071 |
ISBN-13 |
: 9781139069076 |
Rating |
: 4/5 (71 Downloads) |
Synopsis Graph-based Natural Language Processing and Information Retrieval by : Rada Mihalcea
"This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval"--
Author |
: Sheetal S. Sonawane |
Publisher |
: Springer Nature |
Total Pages |
: 186 |
Release |
: 2022-02-22 |
ISBN-10 |
: 9789811699955 |
ISBN-13 |
: 981169995X |
Rating |
: 4/5 (55 Downloads) |
Synopsis Information Retrieval and Natural Language Processing by : Sheetal S. Sonawane
This book gives a comprehensive view of graph theory in informational retrieval (IR) and natural language processing(NLP). This book provides number of graph techniques for IR and NLP applications with examples. It also provides understanding of graph theory basics, graph algorithms and networks using graph. The book is divided into three parts and contains nine chapters. The first part gives graph theory basics and graph networks, and the second part provides basics of IR with graph-based information retrieval. The third part covers IR and NLP recent and emerging applications with case studies using graph theory. This book is unique in its way as it provides a strong foundation to a beginner in applying mathematical structure graph for IR and NLP applications. All technical details that include tools and technologies used for graph algorithms and implementation in Information Retrieval and Natural Language Processing with its future scope are explained in a clear and organized format.
Author |
: Rada Mihalcea |
Publisher |
: |
Total Pages |
: 202 |
Release |
: 2011 |
ISBN-10 |
: OCLC:1137348463 |
ISBN-13 |
: |
Rating |
: 4/5 (63 Downloads) |
Synopsis Graph-based Natural Language Processing and Information Retrieval by : Rada Mihalcea
Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This 2011 book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.
Author |
: Christopher D. Manning |
Publisher |
: Cambridge University Press |
Total Pages |
: |
Release |
: 2008-07-07 |
ISBN-10 |
: 9781139472104 |
ISBN-13 |
: 1139472100 |
Rating |
: 4/5 (04 Downloads) |
Synopsis Introduction to Information Retrieval by : Christopher D. Manning
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
Author |
: Dan Jurafsky |
Publisher |
: Pearson Education India |
Total Pages |
: 912 |
Release |
: 2000-09 |
ISBN-10 |
: 8131716724 |
ISBN-13 |
: 9788131716724 |
Rating |
: 4/5 (24 Downloads) |
Synopsis Speech & Language Processing by : Dan Jurafsky
Author |
: Zhiyuan Liu |
Publisher |
: Springer Nature |
Total Pages |
: 319 |
Release |
: 2020-07-03 |
ISBN-10 |
: 9789811555732 |
ISBN-13 |
: 9811555737 |
Rating |
: 4/5 (32 Downloads) |
Synopsis Representation Learning for Natural Language Processing by : Zhiyuan Liu
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Author |
: Atefeh Farzindar |
Publisher |
: Morgan & Claypool Publishers |
Total Pages |
: 197 |
Release |
: 2017-12-15 |
ISBN-10 |
: 9781681736136 |
ISBN-13 |
: 1681736136 |
Rating |
: 4/5 (36 Downloads) |
Synopsis Natural Language Processing for Social Media by : Atefeh Farzindar
In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact on our daily lives, both on a personal and a professional level. Natural language processing (NLP) is one of the most promising avenues for social media data processing. It is a scientific challenge to develop powerful methods and algorithms which extract relevant information from a large volume of data coming from multiple sources and languages in various formats or in free form. We discuss the challenges in analyzing social media texts in contrast with traditional documents. Research methods in information extraction, automatic categorization and clustering, automatic summarization and indexing, and statistical machine translation need to be adapted to a new kind of data. This book reviews the current research on NLP tools and methods for processing the non-traditional information from social media data that is available in large amounts (big data), and shows how innovative NLP approaches can integrate appropriate linguistic information in various fields such as social media monitoring, healthcare, business intelligence, industry, marketing, and security and defence. We review the existing evaluation metrics for NLP and social media applications, and the new efforts in evaluation campaigns or shared tasks on new datasets collected from social media. Such tasks are organized by the Association for Computational Linguistics (such as SemEval tasks) or by the National Institute of Standards and Technology via the Text REtrieval Conference (TREC) and the Text Analysis Conference (TAC). In the concluding chapter, we discuss the importance of this dynamic discipline and its great potential for NLP in the coming decade, in the context of changes in mobile technology, cloud computing, virtual reality, and social networking. In this second edition, we have added information about recent progress in the tasks and applications presented in the first edition. We discuss new methods and their results. The number of research projects and publications that use social media data is constantly increasing due to continuously growing amounts of social media data and the need to automatically process them. We have added 85 new references to the more than 300 references from the first edition. Besides updating each section, we have added a new application (digital marketing) to the section on media monitoring and we have augmented the section on healthcare applications with an extended discussion of recent research on detecting signs of mental illness from social media.
Author |
: Muskan Garg |
Publisher |
: CRC Press |
Total Pages |
: 272 |
Release |
: 2022-12-28 |
ISBN-10 |
: 9781000789300 |
ISBN-13 |
: 1000789306 |
Rating |
: 4/5 (00 Downloads) |
Synopsis Graph Learning and Network Science for Natural Language Processing by : Muskan Garg
Advances in graph-based natural language processing (NLP) and information retrieval tasks have shown the importance of processing using the Graph of Words method. This book covers recent concrete information, from the basics to advanced level, about graph-based learning, such as neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NPL. It also contains information about language generation based on graphical theories and language models. Features: Presents a comprehensive study of the interdisciplinary graphical approach to NLP Covers recent computational intelligence techniques for graph-based neural network models Discusses advances in random walk-based techniques, semantic webs, and lexical networks Explores recent research into NLP for graph-based streaming data Reviews advances in knowledge graph embedding and ontologies for NLP approaches This book is aimed at researchers and graduate students in computer science, natural language processing, and deep and machine learning.