The emerging function of verb in ections in German language acquisition is modeled with a connectionist network. A network that is initially presented only with a semantic representation of sentences uses the in ectional verb ending -t to mark those sentences that are low in transitivity, whereas all other verb endings occur randomly. This behavior matches an early stage in German language acqu...

Words can shift their meaning across time. This study shows the results obtained by exploratory analysis of semantic shifting on Spanish vocabulary using Diachronic Word Embeddings. data consists a 2018 corpus, before COVID-19 outbreak, and second corpus with documents from 2021. paper addresses construction diachronic word embeddings model, as well non-supervised distance vector technique. The...

With the advent of Semantic Web, knowledge-based interoperability in VE faces a new technological shift, in which ontologies and semantic web technologies plays a major role. Exploiting the explicit semantic description of the domain of discourse allows reasoning and automatically acquiring semantic relations between two different domains of discourses. Such semantic relations would be further ...

Semantic parsing has made significant progress, but most current semantic parsers are extremely slow (CKY-based) and rather primitive in representation. We introduce three new techniques to tackle these problems. First, we design the first linear-time incremental shift-reduce-style semantic parsing algorithm which is more efficient than conventional cubic-time bottom-up semantic parsers. Second...

We report distributional analyses of response times (RT) in two variants of the color-word Stroop task using manual keypress responses. In the classic Stroop task, in which the color and word dimensions are integrated into a single stimulus, the Stroop congruence effect increased across the quantiles. In contrast, in the primed Stroop task, in which the distractor word is presented ahead of col...

Unsupervised domain adaption has recently been used to reduce the shift, which would ultimately improve performance of semantic segmentation on unlabeled real-world data. In this paper, we follow trend propose a novel method shift using strategies discriminator attention and self-training. The strategy contains two-stage adversarial learning process, explicitly distinguishes well-aligned (domai...

in many signal processing applications, an appropriate measure to comparetwo signals plays a fundamental role in both implementing the algorithm andevaluating its performance. several techniques have been introduced in literature assimilarity measures. however, the existing measures are often either impractical forsome applications or they have unsatisfactory results in some other applications....