9783031968624 - bisociative literature-based discovery: methods with tutorials in python de lavra¿, nada; cestnik, bojan; kastrin, andrej (14 resultados)
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This monograph introduces the field of bisociative literature-based discovery (LBD) by first explaining the underlying LBD principles and techniques, followed by the presentation of bisociative LBD techniques and applications developed by the authors. LBD… is a process of uncovering new knowledge by analyzing and connecting disparate pieces of information from different sources of literature.Selected techniques include conventional natural language processing (NLP) approaches, as well as outlier-based, concept-based, network-based, and embeddings-based LBD approaches. Reproducibility aspects of bisociative LBD research are also covered, addressing all steps of the bisociative LBD process: data acquisition, text preprocessing, hypothesis discovery, and evaluation.The monograph is targeted at researchers, students, and domain experts interested in knowledge exploration, information retrieval, text mining, data science or semantic technologies. By covering texts, relations, networks, and ontologies, this work empowers domain experts to transcend their knowledge silos when confronted with varied data formats in their research practice. The monograph s open science approach with tutorials in Python allows for code reuse and experiment replicability.
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Hardcover. Condición: Brand New. 187 pages. 9.25x6.10x9.21 inches. In Stock.
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Idioma: Inglés
Editorial: Springer, Berlin, Springer Nature Switzerland, Springer, 2025
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This monograph introduces the field of bisociative literature-based discovery (LBD) by first explaining the underlying LBD principles and techniques, followed by the presentation of bisociative LBD techniques and applications developed by…the authors. LBD is a process of uncovering new knowledge by analyzing and connecting disparate pieces of information from different sources of literature.Selected techniques include conventional natural language processing (NLP) approaches, as well as outlier-based, concept-based, network-based, and embeddings-based LBD approaches. Reproducibility aspects of bisociative LBD research are also covered, addressing all steps of the bisociative LBD process: data acquisition, text preprocessing, hypothesis discovery, and evaluation.The monograph is targeted at researchers, students, and domain experts interested in knowledge exploration, information retrieval, text mining, data science or semantic technologies. By covering texts, relations, networks, and ontologies, this work empowers domain experts to transcend their knowledge silos when confronted with varied data formats in their research practice. The monograph s open science approach with tutorials in Python allows for code reuse and experiment replicability. 173 pp. Englisch.
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Hardcover. Condición: new. Hardcover. This monograph introduces the field of bisociative literature-based discovery (LBD) by first explaining the underlying LBD principles and techniques, followed by the presentation of bisociative LBD techniques and applications developed by the authors. LBD is a process of uncovering new knowl…edge by analyzing and connecting disparate pieces of information from different sources of literature.Selected techniques include conventional natural language processing (NLP) approaches, as well as outlier-based, concept-based, network-based, and embeddings-based LBD approaches. Reproducibility aspects of bisociative LBD research are also covered, addressing all steps of the bisociative LBD process: data acquisition, text preprocessing, hypothesis discovery, and evaluation.The monograph is targeted at researchers, students, and domain experts interested in knowledge exploration, information retrieval, text mining, data science or semantic technologies. By covering texts, relations, networks, and ontologies, this work empowers domain experts to transcend their knowledge silos when confronted with varied data formats in their research practice. The monographs open science approach with tutorials in Python allows for code reuse and experiment replicability. This monograph introduces the field of bisociative literature-based discovery (LBD) by first explaining the underlying LBD principles and techniques, followed by the presentation of bisociative LBD techniques and applications developed by the authors. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This monograph introduces the field of bisociative literature-based discovery (LBD) by first explaining the underlying LBD principles and techniques, followed by the presentation of bisociative LBD techniques and applications developed by the…authors. LBD is a process of uncovering new knowledge by analyzing and connecting disparate pieces of information from different sources of literature. Selected techniques include conventional natural language processing (NLP) approaches, as well as outlier-based, concept-based, network-based, and embeddings-based LBD approaches. Reproducibility aspects of bisociative LBD research are also covered, addressing all steps of the bisociative LBD process: data acquisition, text preprocessing, hypothesis discovery, and evaluation. The monograph is targeted at researchers, students, and domain experts interested in knowledge exploration, information retrieval, text mining, data science or semantic technologies. By covering texts, relations, networks, and ontologies, this work empowers domain experts to transcend their knowledge silos when confronted with varied data formats in their research practice. The monographs open science approach with tutorials in Python allows for code reuse and experiment replicability.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 188 pp. Englisch.
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