Study on a Vocabulary Learning System Based on Semantic Network

Abstract

A semantic network is made up of a number of circles or nodes which represent objects and description information about those objects. Nodes can be physical items, concepts, events, actions or attributes. The nodes are interconnected by links or arcs. These arcs show the relationships between the various objects and descriptive factors. Knowledge can be represented through using semantic networks. Lexical item learning from contexts of texts is an efficient way better than memorizing many single words. In this paper, we propose a semantic network based vocabulary learning system (SNVLS) which is a knowledge based system. It can analyze the contexts of texts to parse lexical items as objects and relationships for semantic networks and draw the visual graphic networks to improve vocabulary learning efficiency and effect. We study how to use semantic networks to represent contexts for texts. And then we study the architecture and implementation of the SNVLS. At last we present an experiment of using this SNVLS to demonstrate the difference of efficiency and effect in vocabulary learning between two groups who using SNVLS or not