Related Experiment Video
Updated: Jul 23, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
Exploring Representations for Singular and Multi-Concept Relations for Biomedical Named Entity Normalization
Clint Cuffy1, Evan French1, Sophia Fehrmann1
1Virginia Commonwealth University, Richmond, Virginia, USA.
Abstract:
Since the rise of the COVID-19 pandemic, peer-reviewed biomedical repositories have experienced a surge in chemical and disease related queries. These queries have a wide variety of naming conventions and nomenclatures from trademark and generic, to chemical composition mentions. Normalizing or disambiguating these mentions within texts provides researchers and data-curators with more relevant articles returned by their search query. Named entity normalization aims to automate this disambiguation process by linking entity mentions onto their appropriate candidate concepts within a biomedical knowledge base or ontology. We explore several term embedding aggregation techniques in addition to how the term's context affects evaluation performance. We also evaluate our embedding approaches for normalizing term instances containing one or many relations within unstructured texts.
More Related Videos
07:26Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases
Published on: March 19, 2018
07:35A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
Related Concept Videos
Concepts of Health and Illness
Naming Skeletal Muscles
The key factors used in naming muscles include:
Multiple Allele Traits
Drug Nomenclature
Anatomical Terminology
Naming Enantiomers