Sentence examples for semantic divide from inspiring English sources
In this paper, we present a practical, two-phase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach.
Two additional measures idea density (defined as the total number of semantic units divided by total number of words in a speech sample) and efficiency (the total number of semantic units divided by duration of the speech sample in seconds)—were also computed (Ahmed et al., 2013).
Then, we measure the biological quality of the whole interaction mapping as the sum of all the interaction semantic similarities divided by the smaller number of interactions between annotated proteins in the two networks.
The task was designed to divide semantic priming into two additive components, namely facilitation effect and inhibition effect.
The sum of the semantic distances was divided by the number of genes in the profiles' union.
The semantic parts are further divided into sub-parts automatically.
The semantic constraints can be divided into two categories (see Table 4): summarizability and domain constraints.
These filtered distractor candidates are then ranked based on the semantic similarity and further divided into two levels of distractor candidates: low and high, as explained in the "Similarity between correct answer and distractors (SIM Similarity between correct answer anddistractors (SIM)" section.
Declarative memory can be further divided into semantic memory (facts about the world) and episodic memory (the capacity to re-experience an event in the context in which it originally occurred) [ 7, 8].
However, when it comes to large-scale web search engines, the utilization of ontologies in query construction methods is difficult for three reasons [5]: (i) integration is extremely hard, (ii) the web imposes scalability and performance restrictions and (iii) there is a cultural divide between the semantic Web and information retrieval disciplines.
The process of semantic feature mining can be divided into two steps: low-level feature extraction and high-level semantic feature extraction.
