Sentence examples for conceptual vectors from inspiring English sources

As mentioned by Turney (2006), the amount of attributional similarity (i.e., semantic relatedness) between two words, A and B, depends on the degree of correspondence between the properties of A and B. Consequently, there will be some identical parts in the conceptual vectors of A and B. The questions are the following: which parts are the same in the two vectors?

In this paper, we offer a geometric framework for the computing of a concept's conceptual vector based on its similarity position with other concepts in a vector space called concept space, which is a set of concept vectors together with a distance function derived from a similarity model.

If we apply the geometric model to words, the similarity between two words could be calculated from the distance of two attribute vectors in a conceptual space (Gärdenfors 2004).

In fact, nowhere in the cortex is there any real single quantity corresponding to a population vector; rather, a population vector is a computational and conceptual tool serving as a condensed representation of distributed neuronal activity.

This methodology is based on a mathematical framework for carrying out systematic conceptual design of mechanisms using dual-vector algebra.

Basic concepts for designing sliding mode controllers (SMC) are reviewed from the point of view of Filippov vector fields and new conceptual tools are developed.

This condition was dealt with by focusing on right singular vectors that correlate with our conceptual grain-filling pattern, regardless of their singular value's magnitude.

The conceptual approach presented might not apply directly for other vector-borne diseases, whose vectors have different behaviours.

The proposed fuzzy conceptual reduction algorithm is able to reduce the feature vector dimension by 31.3 % compared to the original "best of all combined features" algorithm.

From a conceptual point of view, all notions related to topological vector spaces should match the topology.

It provides conceptual foundations for representing mathematical and physical entities such as scalars, vectors, tensors, physical quantities, physical dimensions and units explicitly designed for knowledge sharing applications in engineering[ 11].