{"product_id":"graph-learning-and-network-science-for-natural-language-processing-9781032224565","title":"Graph Learning and Network Science for Natural Language Processing","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eThe book covers recent advances in graph-based natural language processing (NLP) and information retrieval, including neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NLP. It also discusses language generation based on graphical theories and language models. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 256 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 28 December 2022\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Taylor \u0026amp; Francis Ltd\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eGraph-based natural language processing (NLP) and information retrieval tasks have gained significant importance due to the advancements in graph-based learning. This comprehensive book delves into the latest developments in graph-based learning, encompassing neural network-based approaches, computational intelligence for learning parameters and feature reduction, and network science for graph-based NLP. It also provides valuable insights into language generation based on graphical theories and language models.\u003cbr\u003e\u003cbr\u003eThe book offers a comprehensive exploration of the interdisciplinary graphical approach to NLP, covering various aspects. It discusses recent computational intelligence techniques for graph-based neural network models, such as deep learning and convolutional neural networks. Moreover, it explores advances in random walk-based techniques, semantic webs, and lexical networks, which have played pivotal roles in NLP. The book also delves into recent research into NLP for graph-based streaming data, addressing the challenges of processing and analyzing large-scale graph datasets in real-time. Furthermore, it reviews advances in knowledge graph embedding and ontologies for NLP approaches, enabling more efficient and accurate representation of knowledge and relationships.\u003cbr\u003e\u003cbr\u003eDesigned for researchers and graduate students in computer science, natural language processing, and deep and machine learning, this book provides a thorough understanding of the latest trends and techniques in graph-based learning. It offers valuable insights and practical applications for those working in the field of NLP and related areas.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 660g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 229 x 152 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9781032224565\u003c\/p\u003e","brand":"Shulph Ink","offers":[{"title":"Hardback","offer_id":44104501199098,"sku":"9781032224565","price":122.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/1672408239408_book.jpg?v=1672481142","url":"https:\/\/shulphink.com\/products\/graph-learning-and-network-science-for-natural-language-processing-9781032224565","provider":"Shulph Ink","version":"1.0","type":"link"}