Sentence examples for semantic boundaries from inspiring English sources

It is not the intention to define the term in this paper nor to explore the concept in depth, except to note that there appears to be almost constant redefinition and shifting of semantic boundaries whenever it is expedient to do so — often more in response to political fashion than crop pest reality.

With this understanding of the elusive nature of semantic boundaries, the way is clear to suppose that such boundaries might exist despite their apparent absence.

Arguably we can only know where a semantic boundary lies if that boundary was fixed by our use or by the fact that one particular property is a natural kind.

The inclination to validate all the premises of a sorites argument (along with the inference pattern employed, which the Stoics accepted) was to be explained via ignorance more exactly, the unknowable nature of the relevant sharp semantic boundary.

It subsequently uses the semantic distance boundary spanning measure to demonstrate that boundary spanning innovation has become more common in recent decades, and show that these boundary spanning inventions pose challenges for the traditional specialized-examiner patent examination model.

Here, we propose to counter this effect by combining semantic segmentation with semantically informed edge detection, thus making class boundaries explicit in the model.

This article discusses the importance of boundary spanning innovation, demonstrates the drawbacks of popular metadata based boundary spanning measures, and proposes a new full text semantic similarity measure of boundary spanning.

They found that the faster the RT on a given trial, the further away in neural space the object was represented relative to the boundary between semantic categories.

Therefore, for akhar in Arabic and lagi in Malay which express both Repetition and Increment, we can show its boundaries in the semantic map as follows (Fig. 4): Open image in new window Fig. 4 The boundaries of Arabic akhar and Malay lagi.

Therefore, using the transcript alone to define story boundaries and to create semantic representations for each story becomes difficult.

We show that boundary detection significantly improves semantic segmentation with CNNs in an end-to-end training scheme.