Paper Info
Title
Assessing Student Paraphrases Using Lexical Semantics and Word Weighting
Abstract
We present in this paper an approach to assessing student paraphrases in the intelligent tutoring system iSTART. The approach is based on measuring the semantic similarity between a student paraphrase and a reference text, called the textbase. The semantic similarity is estimated using knowledge-based word relatedness measures. The relatedness measures rely on knowledge encoded in Word-Net, a lexical database of English. We also experiment with weighting words based on their importance. The word importance information was derived from an analysis of word distributions in 2,225,726 documents from Wikipedia. Performance is reported for 12 different models which resulted from combining 3 different relatedness measures, 2 word sense disambiguation methods, and 2 word-weighting schemes. Furthermore, comparisons are made to other approaches such as Latent Semantic Analysis and the Entailer.
Year
DOI
Venue
2009
10.3233/978-1-60750-028-5-165
AIED
Keywords
Field
DocType
semantic similarity,word importance information,weighting word,different model,word weighting,student paraphrase,assessing student,lexical semantics,relatedness measure,word sense disambiguation method,knowledge-based word relatedness measure,different relatedness measure,word distribution,latent semantic analysis,knowledge base,natural language processing
Semantic similarity,Weighting,SemEval,Intelligent tutoring system,Lexical semantics,Computer science,Lexical database,Paraphrase,Natural language processing,Artificial intelligence,Latent semantic analysis
Conference
Volume
ISSN
Citations 
200
0922-6389
9
PageRank 
References 
Authors
1.26
9
4
Name
Order
Citations
PageRank
Vasile Rus1973134.69
Mihai Lintean2987.73
Art Graesser372766.60
Danielle McNamara4333.49