A Local Explainability Technique for Graph Neural Topic Models

A Local Explainability Technique for Graph Neural Topic Models

Abstract Topic modelling is a Natural Language Processing (NLP) technique that has gained popularity in the recent past. It identifies word co-occurrence patterns inside a document corpus to reveal hidden topics. Graph Neural Topic Model (GNTM) is a topic modelling technique that uses Graph Neural N...

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Bibliographic Details
Main Authors: Bharathwajan Rajendran, Chandran G. Vidya, J. Sanil, S. Asharaf
Format: Article
Language:English
Published: Springer Nature 2024-01-01
Series:Human-Centric Intelligent Systems
Subjects:
Explainable neural network
Graph neural topic model
Local explainable
Natural language processing
Topic modelling
Online Access:https://doi.org/10.1007/s44230-023-00058-8

Internet

https://doi.org/10.1007/s44230-023-00058-8

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