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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Empirical distributional semantics: methods and biomedical applications.

Trevor Cohen¹, Dominic Widdows

¹Center for Decision Making and Cognition, Department of Biomedical Informatics, School of Computing and Informatics, Arizona State University, 425 N, 5th Street, Phoenix, AZ 85004-2157, USA. trevor.cohen@asu.edu

Journal of Biomedical Informatics

|February 24, 2009

Summary

This summary is machine-generated.

This study reviews methods for estimating semantic relatedness between terms using natural language text. It covers their development, evaluation in various applications, and recent advancements in computational linguistics and information retrieval.

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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Published on: February 23, 2019

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Computational Linguistics
Cognitive Science
Information Retrieval

Background:

Methods have been developed over 15 years to estimate semantic relatedness between terms.
These methods learn from term distribution in unannotated natural language text.
Evaluations have been conducted in cognitive science, computational linguistics, and information retrieval.

Purpose of the Study:

To review methodologies for deriving semantic relatedness from free text.
To evaluate these methods in biomedical and other applications.
To discuss recent methodological developments and their applicability.

Main Methods:

Review of existing methods for semantic relatedness estimation.
Analysis of evaluation studies across different domains.
Discussion of recent advancements in natural language processing.

Main Results:

A range of methods exist for learning semantic relatedness from text distribution.
These methods have been applied and evaluated in various scientific fields.
Recent developments offer new possibilities for existing applications.

Conclusions:

Semantic relatedness can be effectively learned from unannotated text.
Methodologies are applicable across diverse fields, including biomedical applications.
Ongoing research continues to refine and expand these techniques.