Graph Based Natural Language Processing And Information Retrieval

Author : Rada Mihalcea,Dragomir Radev
Publisher : Cambridge University Press
Page : 202 pages
File Size : 42,6 Mb
Release : 2011-04-11
Category : Computers
ISBN : 0521896134

Get Book

Graph-based Natural Language Processing and Information Retrieval by Rada Mihalcea,Dragomir Radev Pdf

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,Dragomir Radev
Publisher : Cambridge University Press
Page : 201 pages
File Size : 44,7 Mb
Release : 2011-04-11
Category : Computers
ISBN : 9781139498821

Get Book

Graph-based Natural Language Processing and Information Retrieval by Rada Mihalcea,Dragomir Radev Pdf

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 : Unknown
Page : 202 pages
File Size : 47,5 Mb
Release : 2011
Category : Electronic
ISBN : OCLC:1137348463

Get Book

Graph-based Natural Language Processing and Information Retrieval by Rada Mihalcea Pdf

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 : Sheetal S. Sonawane,Parikshit N. Mahalle,Archana S. Ghotkar
Publisher : Springer Nature
Page : 186 pages
File Size : 42,6 Mb
Release : 2022-02-22
Category : Mathematics
ISBN : 9789811699955

Get Book

Information Retrieval and Natural Language Processing by Sheetal S. Sonawane,Parikshit N. Mahalle,Archana S. Ghotkar Pdf

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 : Muskan Garg,Amit Kumar Gupta,Rajesh Prasad
Publisher : CRC Press
Page : 272 pages
File Size : 42,5 Mb
Release : 2022-12-27
Category : Business & Economics
ISBN : 9781000789300

Get Book

Graph Learning and Network Science for Natural Language Processing by Muskan Garg,Amit Kumar Gupta,Rajesh Prasad Pdf

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.

Author : Hang Li
Publisher : Springer Nature
Page : 107 pages
File Size : 43,8 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031021558

Get Book

Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition by Hang Li Pdf

Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work

Author : Zhiyuan Liu,Yankai Lin,Maosong Sun
Publisher : Springer Nature
Page : 319 pages
File Size : 48,5 Mb
Release : 2020-07-03
Category : Computers
ISBN : 9789811555732

Get Book

Representation Learning for Natural Language Processing by Zhiyuan Liu,Yankai Lin,Maosong Sun Pdf

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 : Daniel Bikel,Imed Zitouni
Publisher : IBM Press
Page : 829 pages
File Size : 49,8 Mb
Release : 2012-05-11
Category : Business & Economics
ISBN : 9780137047819

Get Book

Multilingual Natural Language Processing Applications by Daniel Bikel,Imed Zitouni Pdf

Multilingual Natural Language Processing Applications is the first comprehensive single-source guide to building robust and accurate multilingual NLP systems. Edited by two leading experts, it integrates cutting-edge advances with practical solutions drawn from extensive field experience. Part I introduces the core concepts and theoretical foundations of modern multilingual natural language processing, presenting today’s best practices for understanding word and document structure, analyzing syntax, modeling language, recognizing entailment, and detecting redundancy. Part II thoroughly addresses the practical considerations associated with building real-world applications, including information extraction, machine translation, information retrieval/search, summarization, question answering, distillation, processing pipelines, and more. This book contains important new contributions from leading researchers at IBM, Google, Microsoft, Thomson Reuters, BBN, CMU, University of Edinburgh, University of Washington, University of North Texas, and others. Coverage includes Core NLP problems, and today’s best algorithms for attacking them Processing the diverse morphologies present in the world’s languages Uncovering syntactical structure, parsing semantics, using semantic role labeling, and scoring grammaticality Recognizing inferences, subjectivity, and opinion polarity Managing key algorithmic and design tradeoffs in real-world applications Extracting information via mention detection, coreference resolution, and events Building large-scale systems for machine translation, information retrieval, and summarization Answering complex questions through distillation and other advanced techniques Creating dialog systems that leverage advances in speech recognition, synthesis, and dialog management Constructing common infrastructure for multiple multilingual text processing applications This book will be invaluable for all engineers, software developers, researchers, and graduate students who want to process large quantities of text in multiple languages, in any environment: government, corporate, or academic.

Author : Anne Kao,Steve R. Poteet
Publisher : Springer Science & Business Media
Page : 272 pages
File Size : 52,6 Mb
Release : 2007-03-06
Category : Computers
ISBN : 9781846287541

Get Book

Natural Language Processing and Text Mining by Anne Kao,Steve R. Poteet Pdf

Natural Language Processing and Text Mining not only discusses applications of Natural Language Processing techniques to certain Text Mining tasks, but also the converse, the use of Text Mining to assist NLP. It assembles a diverse views from internationally recognized researchers and emphasizes caveats in the attempt to apply Natural Language Processing to text mining. This state-of-the-art survey is a must-have for advanced students, professionals, and researchers.

Author : Elisabeth Métais,Farid Meziane,Mohamad Saraee,Vijayan Sugumaran,Sunil Vadera
Publisher : Springer
Page : 488 pages
File Size : 40,5 Mb
Release : 2016-06-16
Category : Computers
ISBN : 9783319417547

Get Book

Natural Language Processing and Information Systems by Elisabeth Métais,Farid Meziane,Mohamad Saraee,Vijayan Sugumaran,Sunil Vadera Pdf

This book constitutes the refereed proceedings of the 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, held in Salford, UK, in June 2016. The 17 full papers, 22 short papers, and 13 poster papers presented were carefully reviewed and selected from 83 submissions. The papers cover the following topics: theoretical aspects, algorithms, applications, architectures for applied and integrated NLP, resources for applied NLP, and other aspects of NLP.

Author : Muskan Garg,Sandeep Kumar,Abdul Khader Jilani Saudagar
Publisher : CRC Press
Page : 271 pages
File Size : 41,5 Mb
Release : 2023-11-28
Category : Computers
ISBN : 9781003800484

Get Book

Natural Language Processing and Information Retrieval by Muskan Garg,Sandeep Kumar,Abdul Khader Jilani Saudagar Pdf

This book presents the basics and recent advancements in natural language processing and information retrieval in a single volume. It will serve as an ideal reference text for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology. This text emphasizes the existing problem domains and possible new directions in natural language processing and information retrieval. It discusses the importance of information retrieval with the integration of machine learning, deep learning, and word embedding. This approach supports the quick evaluation of real-time data. It covers important topics including rumor detection techniques, sentiment analysis using graph-based techniques, social media data analysis, and language-independent text mining. Features: • Covers aspects of information retrieval in different areas including healthcare, data analysis, and machine translation • Discusses recent advancements in language- and domain-independent information extraction from textual and/or multimodal data • Explains models including decision making, random walk, knowledge graphs, word embedding, n-grams, and frequent pattern mining • Provides integrated approaches of machine learning, deep learning, and word embedding for natural language processing • Covers latest datasets for natural language processing and information retrieval for social media like Twitter The text is primarily written for graduate students and academic researchers in interdisciplinary areas of electrical engineering, electronics engineering, computer engineering, and information technology.

Author : Troels Andreasen,Guy De Tré,Janusz Kacprzyk,Henrik Legind Larsen,Gloria Bordogna,Sławomir Zadrożny
Publisher : Springer Nature
Page : 245 pages
File Size : 51,7 Mb
Release : 2021-09-15
Category : Computers
ISBN : 9783030869670

Get Book

Flexible Query Answering Systems by Troels Andreasen,Guy De Tré,Janusz Kacprzyk,Henrik Legind Larsen,Gloria Bordogna,Sławomir Zadrożny Pdf

This book constitutes the refereed proceedings of the 14th International Conference on Flexible Query Answering Systems, FQAS 2021, held virtually and in Bratislava, Slovakia, in September 2021. The 16 full papers and 1 perspective papers presented were carefully reviewed and selected from 17 submissions. They are organized in the following topical sections: model-based flexible query answering approaches and data-driven approaches.

Author : Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze
Publisher : Cambridge University Press
Page : 128 pages
File Size : 51,6 Mb
Release : 2008-07-07
Category : Computers
ISBN : 9781139472104

Get Book

Introduction to Information Retrieval by Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze Pdf

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 : Alessandro Nego
Publisher : Simon and Schuster
Page : 494 pages
File Size : 43,8 Mb
Release : 2021-09-28
Category : Computers
ISBN : 9781617295645

Get Book

Graph-Powered Machine Learning by Alessandro Nego Pdf

At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You'll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you'll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls. Key Features · The lifecycle of a machine learning project · Three end-to-end applications · Graphs in big data platforms · Data source modeling · Natural language processing, recommendations, and relevant search · Optimization methods Readers comfortable with machine learning basics. About the technology By organizing and analyzing your data as graphs, your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where it's important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning. Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.

Author : Dan Jurafsky
Publisher : Pearson Education India
Page : 912 pages
File Size : 55,6 Mb
Release : 2000-09
Category : Electronic
ISBN : 8131716724

Get Book

Speech & Language Processing by Dan Jurafsky Pdf