Title :
Automated Scoring System Using Dependency-Based Weighted Semantic Similarity Model
Author :
Chen, Liang ; Liu, Yajun
Author_Institution :
Sch. of Comput. Sci. & Eng., Southeast Univ., Nanjing, China
fDate :
Nov. 30 2009-Dec. 1 2009
Abstract :
Traditionally, automated scoring system uses semantic similarity between words and the weight of words to calculate semantic similarity between student´s answer and standard answer. It doesn´t consider the word-order or syntactic information, which can improve the knowledge representation and therefore lead to better performance. This article presents a novel approach called dependency-based weighted semantic similarity model which takes syntactic relations into account and incorporates word-based information in addition to dependency parsing. The experiment shows that compared with traditional word-based weighted semantic similarity model, the dependency-based weighted semantic similarity model improves the precision obviously. It also provides better discrimination of syntactic-semantic knowledge representation than the traditional one.
Keywords :
educational technology; knowledge representation; automated scoring system; dependency parsing; dependency-based weighted semantic similarity model; syntactic-semantic knowledge representation; word-based information; Automatic testing; Binary trees; Computer science; Electronic mail; Feedback; Knowledge acquisition; Knowledge engineering; Knowledge representation; Performance evaluation; System testing; Automated scoring system; Dependency Relation; Relation Path; dependency parsing; weighted semantic similarity model;
Conference_Titel :
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3888-4
DOI :
10.1109/KAM.2009.77