DOI: http://dx.doi.org/10.26483/ijarcs.v9i2.5783 Volume 9, No. 2, March-April 2018 International Journal of Advanced Research in Computer Science RESEARCH PAPER Available Online at www.ijarcs.info © 2015-19, IJARCS All Rights Reserved 714 ISSN No. 0976-5697 THEORETICAL ANSWER EVALUATION USING LSA, BLEU, WMD AND FUZZY LOGIC Praful Mishra K.J Somaiya Institute Of Engineering And Information Technology, Sion, Mumbai, India Anmol Mishra K.J Somaiya Institute Of Engineering And Information Technology, Sion, Mumbai, India Aniket Bharti K.J Somaiya Institute Of Engineering And Information Technology, Sion, Mumbai, India Prof. Sarita Ambadekar K.J Somaiya Institute Of Engineering And Information Technology, Sion, Mumbai, India Abstract : Assessment of student answers to grade their overall understanding of a subject is a critical task. However grading can be monotonous and sometimes can be tedious task for the teachers. Automatic Grading can reduce tedium on teachers but it is complicated by free form student inputs. The main task of automatic grading system is to assign ordinal scores to student answers, based on “model” or ideal answers. Here we introduce a novel framework comprising of three building blocks Word Mover Distance(WMD)a statistical model Latent Semantic Analysis(LSA),Bilingual Evaluation Understudy(BLEU) and Fuzzy logic, a model based on degree of truth to output scores. In other words LSA is used to identify the semantic similarity between two concepts. Word Mover’s Distance (WMD), uses vector encoding of words to calculate the minimum cumulative distance that words from a reference solution need to travel to match words from a student answer. This cumulative distance assess the distance between two documents in a meaningful way, even when they have no words in common. Fuzzy logic is a primitive model in this system which is used to output the final score based on inputs which are the outputs of LSA and WMD. This proposed method gives better precision, enhanced dependability of results, thus saving the effort and time of staff. Keywords: Latent Semantic Analysis, Singular Value Decomposition, N-gram, Word Mover Distance, Fuzzy Logic. I. INTRODUCTION Theory based examinations are held periodically to assess students academically.The purpose of these assessment is to gain insight of student understanding and knowledge enhancement. However the manual evaluation of answers sometimes can be monotonous ,bias errors and tiring. To overcome these difficulties a faster and reliable method to evaluate answer is required. Natural Language Processing (NLP) is a technique in Artificial Intelligence that enables us to analyze and synthesize natural language. NLP is further divided into syntactic analysis and semantic analysis. Latter is used for analysing grammar and arrangement of words in such a manner that they show relationship among themselves. While former is used to extract meaning from text. We publish a paper that uses both syntactic and semantic methods to evaluate student’s answer and allot them marks. This paper proposes a algorithm to avoid this gruesome manual answer evaluation. The assessment of answers is done using a novel framework comprising of generative probabilistic technique and degree of truth techniques. Due to the freedom of input students write a particular sentence in various form.LSA[1] measures the semantic similarity of these answers with standard answer by finding out important topics in both, BLEU[2]avoids overrating of answer by LSA. WMD[4] measures the similarity of students answer with standard answers even if sentences in both are written different way but mean the same. LSA,BLEU,WMD along with soft computing technique Fuzzy Logic[3] gives the overall assessment of student and standard answer. II. EXISTING SYSTEM Attali and Burstein devloped E-rater (Electronic Essay Grade), that checks the writing style and the structure of the essays rather than the specific content. Text features like vocabulary level used, word occurring probability, correlation, word length and essay length were extracted and parsed using MSNLP (Microsoft Natural Language Processing) tool with training essays. These features are allotted weightage. Already graded essay are used for evaluating new essays, its features are compared to already graded essays. The strength is the agreement between the E-rater and human is above 97%. The weakness of E-rater is that it requires a number of manually scored training essays to score the answers. C-rater (Concept rater) (Burstein et al., 2001; Yigal et al., 2008) was also developed by ETS (Educational Testing Service) and it is also called as content rater. The scoring is based on the content and concept. It uses natural language