Topic-Weighted Kernels: Text Kernels Integrating Topic Weights and Deep Word Embeddings for Semantic Text Analytics

Topic-Weighted Kernels: Text Kernels Integrating Topic Weights and Deep Word Embeddings for Semantic Text Analytics

Traditional text classification models, such as text kernels, primarily consider the syntactic aspects of text data. This paper introduces Topic-Weighted Kernels, a new text analytics framework that combines global topical themes with word-level semantics in a text kernel architecture. Three new tex...

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Bibliographic Details
Main Authors: Nikhil V. Chandran, V. S. Anoop, S. Asharaf
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Deep word embeddings
latent Dirichlet allocation
text kernels
BERT
topic modeling
Word2Vec
Online Access:https://ieeexplore.ieee.org/document/10980292/
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https://ieeexplore.ieee.org/document/10980292/

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