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...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Nikhil V. Chandran, V. S. Anoop, S. Asharaf
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	IEEE 2025-01-01
Sarja:	IEEE Access
Aiheet:	Deep word embeddings latent Dirichlet allocation text kernels BERT topic modeling Word2Vec
Linkit:	https://ieeexplore.ieee.org/document/10980292/
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