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Springer - Bisociative Literature-Based Discovery Python Book

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Description

Explore the field of bisociative literature-based discovery (LBD) with this technical monograph from Springer. This book explains the fundamental principles and techniques of LBD, a process designed to uncover new knowledge by connecting disparate information from various literature sources. Readers will learn about diverse methodologies, including conventional natural language processing (NLP) approaches and more advanced techniques such as outlier-based, concept-based, network-based, and embeddings-based LBD. The text includes tutorials in Python to help apply these methods practically. This resource is designed for those interested in the intersection of computer science, AI, and machine learning through the lens of literature-based discovery.

Key Features

This Springer monograph introduces the principles and techniques of bisociative literature-based discovery (LBD).

Learn to uncover new knowledge by connecting disparate information from different literature sources.

Includes practical tutorials in Python to implement bisociative LBD techniques and applications.

Covers various methodologies including natural language processing (NLP) and embeddings-based approaches.

Explores specialized LBD methods such as outlier-based, concept-based, and network-based techniques.

Product Specifications

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

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