Sentence examples for use of semantic features from inspiring English sources

Taking this into account, this paper proposes a middleware-oriented integrated architecture with the use of semantic features (concretely with the use of Linked Data) as data provider to offer a brokerage service for the procurement of products in a Supply Chain Management (SCM).

Christensen et al. [22, 23] investigate the use of semantic features for the task of Open IE, specifically the application of Semantic Role Labeling SRLL).

Similar low-level features can also be used to explicitly predict the presence of semantic features including grass and sky.

We extend a recent approach in which a single neighborhood structure is automatically designed from a Mixed Integer Programming model using a combination of automatic extraction of semantic features and automatic algorithm configuration.

Just like the set of DRs, an alternative set of semantic features can be used as the coordinate axes to construct the corresponding vector set for D. Applying spectral clustering, a matrix analogs to the DRDA matrix can be constructed according to the steps described in Section 4. Including the proposed DR, 5 sets of features are investigated.

To determine the optimal number of semantic features, different numbers of semantic features are used to retrieve images.

The classification analysis was then performed using the semantic-feature predictors and the residuals to show unique effects of semantic features that cannot be accounted for by the HMax model.

The best approximation for the multitude of semantic features associated with each target object was obtained by using a list of behaviorally produced object features from the Centre for Speech Language and the Brain dataset (henceforth, the CSLB features25).

The recorded brain patterns are best decoded using a combination of not only the three semantic features that were in fact presented as clues, but a far richer set of semantic features typically linked to the target object.

The brain activation patterns for each trial were predicted using semantic coordinates obtained via different models: (1) the last clue of each triplet ("Clue 3"), (2) sum of the three clues of the triplet ("Clue 1 + 2 + 3"), (3) the target object and (4) sum of the full list of semantic features typically associated with the target object ("All available features").

(c) Hidden semantic features: s k is the hidden semantic feature, s k ∈ S = {s 1,...,s K }, where K is the total number of semantic features.  .