Sentence examples for semantic correlations from inspiring English sources
"semantic correlations" is correct and usable in written English
This phrase can be used in a variety of contexts, such as when discussing how one concept can be related to another concept based on meaning, or when discussing how two words or phrases can be related to one another semantically. For example, "The semantic correlations between 'anticipation' and 'excitement' are evident in everyday conversation."
English, as an inflectional language, makes heavy use of derivational morphology to signal semantic correlations (e.g. interesting vs. interested).
The sequence of QA pair representations are then fed into the RNNs to model the semantic correlations among answers.
Semantic associations were represented in the LO and pF, while the PPA was involved in semantic correlations and spatial characteristics, which were sensitive to the openness of scenes.
Based on the semantic correlations implied by the taint path information, this paper presents an algorithm for extracting the structures of unknown file formats.
It combines convolutional neural network (CNN) with recurrent neural network (RNN) to capture both the semantic matching between question and answer and the semantic correlations embedded in the sequence of answers.
On the other side, although two entities have strong semantic correlations, they might not be extracted from literature because of their low cooccurrence frequency in literature.
In addition, Zhu et al. [46] propose a topic hypergraph hashing for mobile image retrieval, which learns hashing codes with high order semantic correlation preserving, and simultaneously leverages the associated textual modality to enrich semantics of hashing codes.
As a result, our model is able to discover more accurate semantic correlation between textual tags and visual features, and finally providing favourable visual semantics interpretation even with highly sparse and incomplete tags.
Crowdsourced data from social multimedia applications hosts thousands of diversified semantic tags, which allow many systems to rely on tag frequency and semantic correlation to create unsupervised annotations.
Thus, the hybrid deep learning architecture can capture semantic correlation among visual words and consequently minimizes the semantic loss associated with BoVW-based image retrieval.
The Security Business Flow policy modeler consists of a policy editor, a VM security configurator and a semantic correlation modeler to enable cloud user to design SBF and security policies.
