Advances on Graph-Based Approaches in Information Retrieval
Title | Advances on Graph-Based Approaches in Information Retrieval PDF eBook |
Author | Ludovico Boratto |
Publisher | Springer Nature |
Pages | 98 |
Release | |
Genre | |
ISBN | 3031713826 |
Graph Learning and Network Science for Natural Language Processing
Title | Graph Learning and Network Science for Natural Language Processing PDF eBook |
Author | Muskan Garg |
Publisher | CRC Press |
Pages | 272 |
Release | 2022-12-28 |
Genre | Business & Economics |
ISBN | 1000789306 |
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.
Graph-based Natural Language Processing and Information Retrieval
Title | Graph-based Natural Language Processing and Information Retrieval PDF eBook |
Author | Rada Mihalcea |
Publisher | Cambridge University Press |
Pages | 201 |
Release | 2011-04-11 |
Genre | Computers |
ISBN | 1139498827 |
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.
Graph-Based Methods in Computer Vision: Developments and Applications
Title | Graph-Based Methods in Computer Vision: Developments and Applications PDF eBook |
Author | Bai, Xiao |
Publisher | IGI Global |
Pages | 395 |
Release | 2012-07-31 |
Genre | Computers |
ISBN | 1466618922 |
Computer vision, the science and technology of machines that see, has been a rapidly developing research area since the mid-1970s. It focuses on the understanding of digital input images in many forms, including video and 3-D range data. Graph-Based Methods in Computer Vision: Developments and Applications presents a sampling of the research issues related to applying graph-based methods in computer vision. These methods have been under-utilized in the past, but use must now be increased because of their ability to naturally and effectively represent image models and data. This publication explores current activity and future applications of this fascinating and ground-breaking topic.
Advances on Graph-Based Approaches in Information Retrieval
Title | Advances on Graph-Based Approaches in Information Retrieval PDF eBook |
Author | Ludovico Boratto |
Publisher | Springer |
Pages | 0 |
Release | 2024-10-15 |
Genre | Computers |
ISBN | 9783031713811 |
This book constitutes the refereed proceedings of the First International Workshop on Graph-Based Approaches in Information Retrieval, IRonGraphs 2024, held in Glasgow, UK, on March 24, 2024. The 6 full papers included in this book were carefully reviewed and selected from 14 submissions. They focus on diverse novel contributions, with presentations on knowledge-aware graph-based recommender systems using user-based semantic features filtering, source-target node distance impacts on adversarial attacks in social network recommendations.
Graph-based Knowledge Representation
Title | Graph-based Knowledge Representation PDF eBook |
Author | Michel Chein |
Publisher | Springer Science & Business Media |
Pages | 428 |
Release | 2008-10-20 |
Genre | Mathematics |
ISBN | 1848002866 |
This book provides a de?nition and study of a knowledge representation and r- soning formalism stemming from conceptual graphs, while focusing on the com- tational properties of this formalism. Knowledge can be symbolically represented in many ways. The knowledge representation and reasoning formalism presented here is a graph formalism – knowledge is represented by labeled graphs, in the graph theory sense, and r- soning mechanisms are based on graph operations, with graph homomorphism at the core. This formalism can thus be considered as related to semantic networks. Since their conception, semantic networks have faded out several times, but have always returned to the limelight. They faded mainly due to a lack of formal semantics and the limited reasoning tools proposed. They have, however, always rebounded - cause labeled graphs, schemas and drawings provide an intuitive and easily und- standable support to represent knowledge. This formalism has the visual qualities of any graphic model, and it is logically founded. This is a key feature because logics has been the foundation for knowledge representation and reasoning for millennia. The authors also focus substantially on computational facets of the presented formalism as they are interested in knowledge representation and reasoning formalisms upon which knowledge-based systems can be built to solve real problems. Since object structures are graphs, naturally graph homomorphism is the key underlying notion and, from a computational viewpoint, this moors calculus to combinatorics and to computer science domains in which the algorithmicqualitiesofgraphshavelongbeenstudied,asindatabasesandconstraint networks.
Information Retrieval and Natural Language Processing
Title | Information Retrieval and Natural Language Processing PDF eBook |
Author | Sheetal S. Sonawane |
Publisher | Springer Nature |
Pages | 186 |
Release | 2022-02-22 |
Genre | Mathematics |
ISBN | 981169995X |
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.