Siti Khaotijah Mohammad, A Subjective Question and Answer System: An Implementation of Stemming, Stop List, Hamming, Latent Semantic Indexing, Log-Likelihood Ratio Summarization and Vector Space Model Algorithms

Background: There are only few systems have been developed for the SQ&A System. This is because the subjective questions did not have definite evaluation standard and examiners had different evaluation standard for different students. Objective: The Subjective Question and Answer System (SQ&A System) is a system that provides a platform for examiners to automatically grade their subjective examinations' questions. The algorithms used are Stemming, Stop List, Hamming, Latent Semantic Indexing (LSI), Log-likelihood Ratio and Vector Space Model (VSM). These algorithms were used to process the user's input as the answer and determine its relevancy according to the answer stored in the question and answer system database. Stemming algorithm is used to improve retrieval effectiveness and to reduce the size of indexing files. LSI is used to check the spelling mistake of the answer that consists of one word and Log-Likelihood ratio is for summarization. Vector Space Model is used to ensure that the performance of the system component is high by effective implementation of data type, data searching algorithms, sorting algorithms and mathematical equations. Results: Comparison using four users shows significant different between the manual evaluation and SQ&A system. Conclusion: Implementing the Hamming algorithm and LSI had increased the accuracy of answer evaluation as well as enhanced the SQ&A system. However, there is a significant different between the SQ&A system with manual evaluation due to limited synonyms words in the dictionary