£115.87

Springer Bisociative Literature-Based Discovery Python Book

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Description

Explore the field of bisociative literature-based discovery (LBD) with this specialized monograph from Springer. This book provides a deep look into the principles and techniques used to uncover new knowledge by connecting disparate information from different literature sources. Readers will learn how to analyze and link separate pieces of data to find hidden connections. The text covers a wide range of methods, including conventional natural language processing (NLP) approaches. It also introduces advanced techniques such as outlier-based, concept-based, network-based, and embeddings-based LBD approaches. Designed for those interested in AI and machine learning, the book includes Python tutorials to support practical application. This resource is built with a focus on reproducibility, making it a valuable asset for researchers and developers working with complex data sets and automated knowledge discovery.

Key Features

Covers foundational LBD principles and techniques to help you understand how to connect disparate information from various literature sources.

Includes practical Python tutorials to assist with implementing bisociative literature-based discovery methods in your own projects.

Explores diverse technical approaches including natural language processing (NLP), outlier-based, and concept-based methods.

Provides advanced insights into network-based and embeddings-based LBD approaches for modern data analysis.

Focuses on reproducibility aspects to ensure that the methods and applications presented can be applied effectively in research settings.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
08 August 2025
Listed Since
16 May 2025

Barcode

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