We examine the nature of phonological and semantic similarity in early language learning and how the use of this information might change over the course of development. To this end, we represent the lexicon as either a phonological or semantic network and model the growth of this network. Constructing normative vocabularies from the Communicative Development Inventory norms (CDI), we utilize a...

In recent work exploring the semantic fluency task, we found evidence indicative of optimal foraging policies in memory search that mirror search in physical environments. We determined that a 2-stage cue-switching model applied to a memory representation from a semantic space model best explained the human data. Abbott, Austerweil, and Griffiths demonstrate how these patterns could also emerge...

The N400 component of the event-related brain potential is widely used in research on language and semantic memory, but the cognitive functions underlying N400 amplitudes are still unclear and actively debated. Recent simulations with a neural network model of word meaning suggest that N400 amplitudes might reflect implicit semantic prediction error. Here, we extend these simulations to sentenc...

The linguistic and conceptual shift in Terminology has led to a more discourse-centered approach with a focus on how terms are used in texts (Temmerman and Kerremans, 2003). This shift has affected the construction of terminological knowledge bases, which have an underlying network of semantic relations. Such a network can be derived from corpus analysis and the extraction of terminological uni...

This article presents the issues of knowledge management, in particular knowledge acquisition. The article summarizes research work started with the SeiPro2S (Semantically Enhanced Intellectual Property Protection System) system designed to protect resources from the unauthorized use of intellectual property. The system implements semantic network as a structure of knowledge representation and ...

This paper presents a model of the mental lexicon and its formation, based on the self-organizing neural network. When exposed to raw text, the model clusters words according to their semantic relatedness to form a semantic network 7]. Simulations using artiicial data are described that show how co-occurrence information can be used to create a low-dimensional representation of lexical semantic...

Semantic Networks (SN) have been used in many applications, especially in the field of natural language understanding (NLU). The multflayered extended semantic network MESNET presented in this paper on the one hand follows the tradition of semantic networks (SN) starting with the work of Quillian (13). On the other hand, MESNET for the first time consequently and explicitly makes use of a multi...

This paper presents a deep learning architecture for the semantic decoder component of a Statistical Spoken Dialogue System. In a slot-filling dialogue, the semantic decoder predicts the dialogue act and a set of slot-value pairs from a set of n-best hypotheses returned by the Automatic Speech Recognition. Most current models for spoken language understanding assume (i) word-aligned semantic an...