A reassessment of category-specific semantic deficits in light of their contribution to a theory of the representation of lexical concepts is proposed. Two theories are examined: one, held by the majority of researchers in the field, claims that concepts are represented by sets of features; another, in contrast, claims that concepts are atomic representations. An analysis of category-specific s...

Verbal word formation processes involving prefixes and particles are highly productive in Germanic languages. The compositional semantics of such prefix and particle verbs requires an in-depth analysis of the interdependence of their constituent parts for adequately representing these types of complex verbs in lexical-semantic networks. The present paper introduces modeling principles that acco...

Semantic strategies are a kind of discourse strategy that include the sum of language and cognitive moves which are used to reach an adequate goal of communication normally resulting in text comprehension by the reader or listener. Here, the language user takes a number of steps in order to perform a complex task. Semantic strategies in prejudiced talk have been examined extensively in western ...

Semantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches to computing semantic similarity by mapping terms to an ontology and by examining their relationships in that ontology. More specifically, we investigate approaches to computing the semantic similarity between natural language terms (using WordN...

The aim of this study is to determine the effect of word clustering method on vocabulary learning of Iranian EFL learners through a case of semantic versus phonological clustering. To this effect, 80 homogeneous students from four intermediate classes at an English institute in Torbat e Heydariyeh participated in this research. They were assigned to four groups according to semantic versus phon...

The UOW submissions to the Semantic Textual Similarity task at SemEval-2012 use a supervised machine learning algorithm along with features based on lexical, syntactic and semantic similarity metrics to predict the semantic equivalence between a pair of sentences. The lexical metrics are based on wordoverlap. A shallow syntactic metric is based on the overlap of base-phrase labels. The semantic...

In this paper, we develop a new way of creating sense vectors for any dictionary, by using an existing word embeddings model, and summing the vectors of the terms inside a sense’s definition, weighted in function of their part of speech and their frequency. These vectors are then used for finding the closest senses to any other sense, thus creating a semantic network of related concepts, automa...

Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we introduced a novel attractor network model of automatic semantic prim...

Semantic Similarity relates to computing the similarity between concepts (terms) which are not necessarily lexically similar. We investigate approaches to computing semantic similarity by mapping terms to an ontology and by examining their relationships in that ontology. More specifically, we investigate approaches to computing the semantic similarity between natural language terms (using WordN...