Sentence examples for semantic measures from inspiring English sources

The application of the proposed framework and its practical usefulness is underlined by an empirical analysis of hundreds of semantic measures in a biomedical context.

PosetHERep and HESML are motivated by several drawbacks in the current semantic measures libraries, especially the performance and scalability, as well as the evaluation of new methods and the replication of most previous methods.

As a result, this paper presents a unifying framework that aims to improve the understanding of semantic measures, to highlight their equivalences and to propose bridges between their theoretical bases.

By training a model which incorporated both visual and semantic measures, we were able to successfully construct neural representations for untrained objects with a high degree of accuracy.

For each object, we obtained measures capturing the low and high-level visual attributes of the picture in addition to feature-based semantic measures.

In addition to these semantic measures, we also implemented the Jaccard similarity coefficient between the sets of vertices in the two annotation graphs (Supplementary Materials).

However, it does not reflect the semantic measure among the items.

It has been empirically proved that a sentence similarity measure performs better when the semantic measure is weighted more than the syntactic one.

Our objective was to determine whether a standard semantic measure (i.e., category fluency [CAT] to animals, fruits, and vegetables) adds independent information above and beyond Aβ-related decline captured by the PACC.

The semantic measure of an itemset is characterized with utility values that are typically associated with transaction items, where a user will be interested to an itemset only if it satisfies a given utility constraint.

We evaluated functional similarities for annotated Dmel DPGs using the Resnik semantic measure [ 34].