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We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description.
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In this way, semantic topics generate tags or classes for images with each defining a prior distribution in visual topic space.
We develop a topic-driven initialization scheme, so that each learned relation type can be interpreted as a representative of semantic topics of the objects.
'IXN' and 'TOPIC' mean semantic and topic features, respectively.
Along with semantic features, topic features are newly added to address the Triage problem.
> -wrap-foot> The proposed method in the Triage task includes new feature types: semantic and topic features.
> -wrap-foot> Tables 3 and 4 show the average precision changes when semantic and topic features are added to word features.
"We're using a non-semantic neural topic modeling approach with a heuristic function for hierarchy building," says Schjøll Brede.
For example, if the non-seed topic semantic type gngm occurs with the predicate ASSOCIATED_WITH 107 times, and all combined non-seed semantic types occur with the same predicate 171 times, the resulting RlogF score will be 4.22.
Existing solutions commonly use text clustering, association rule mining, or latent semantic models for topic discovery.
The five analytical techniques are cosine similarity using term frequency-inverse document frequency vectors (tf-idf cosine), latent semantic analysis (LSA), topic modeling, and two Poisson-based language models – BM25 and PMRA (PubMed Related Articles).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com