Abstract
Based on the method of latent semantic analysis (LSA), a text matching algorithm was proposed in this paper for constructing an automated scoring system of Chinese test papers. Firstly, by fully considering the correlation between terms, texts of examinee's answers and standard answers were represented in lower-dimensional space and the model was improved using the way of singular value decomposition. Secondly, using LSA, the cosine similarity between the texts of examinee's answers and standard answers was taken as similarity criterion to determine the score of answer of each examination question. Experimental results show that by considering the semantic information of text, the proposed algorithm has satisfactory scoring results, and the work presented in this paper is a beneficial exploration for implementing semantic based automated scoring system.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
