Word finding difficulty is a frequently reported subjective cognitive concern among persons with Multiple Sclerosis (pwMS). Word-finding relies on several information retrieval processes, including search and retrieval from the conceptual store, the phonological store, the syllabary, as well as other stores of information. Neuropsychological…

We examined 3 different network models of representing semantic knowledge (5,018-word directed and undirected step distance networks, and an association-correlation network) to predict lexical priming effects. In Experiment 1, participants made semantic relatedness judgments for word pairs with varying path lengths. Response latencies for…

Human word learning is remarkable: We not only learn thousands of words but also form organized semantic networks in which words are interconnected according to meaningful links, such as those between "apple," "juicy," and "pear." These links play key roles in our abilities to use language. How do words become…

In this study, the authors examined the impact of a vocabulary intervention designed to support vocabulary depth, or the building of semantic networks, in preschool children (n = 30). The authors further investigated the effect of specific instructional strategies on growth in vocabulary depth. The intervention employed shared book reading and…

Despite their drawback, multiple-choice questions are an enduring feature in instruction because they can be answered more rapidly than open response questions and they are easily scored. However, it can be difficult to generate good incorrect choices (called "distractors"). We designed an algorithm to generate distractors from a…

In this study, the authors examined the impact of a vocabulary intervention designed to support vocabulary depth, or the building of semantic networks, in preschool children (n = 30). The authors further investigated the effect of specific instructional strategies on growth in vocabulary depth. The intervention employed shared book reading and…

Recent semantic space models learn vector representations for word meanings by observing statistical redundancies across a text corpus. A word's meaning is represented as a point in a high-dimensional semantic space, and semantic similarity between words is quantified by a function of their spatial proximity (typically the cosine of the angle…

We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network…

Just as language reflects one's thoughts, the text of science textbooks reflects the structure of scientific knowledge and thought. Therefore, students' learning of scientific language leads to their acquisition of the structure of scientific knowledge and thought. The purposes of this study were to extract scientific semantic network from science…

Discusses semantic communication networks, methods of content analysis, and voice mail as a communication system. A study that examined conceptual distinctions between voice mail answering and voice mail messaging in a large insurance organization is described, and semantic network analysis is compared to traditional content analysis. (28…