NDLI: Study on a Vocabulary Learning System Based on Semantic Network

Content Provider	IEEE Xplore Digital Library
Author	Li Mengyu Hu Daiping Li Zongming Lei Aizhong
Copyright Year	2006
Description	Author affiliation: Shanghai Xiandai Vocational & Tech. Sch. (Li Mengyu)
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
Starting Page	1004
Ending Page	1008
File Size	5410714
Page Count	5
File Format	PDF
ISBN	1424403170
DOI	10.1109/SOLI.2006.328888
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vocabulary Shape Educational institutions Information retrieval semantic network Tellurium Software development management Electrooptic effects knowledge based system Learning systems Graphics Frequency locked loops vocabulary learning
Content Type	Text
Resource Type	Article

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