In NLP, the challenging and crucial task is Word Sense Disambiguation (WSD). Many Natural Languages have many ambiguous words with more than one sense. Depending on the context the sense of the ambiguous word is identified, this process is termed as word sense disambiguation (WSD). WSD algorithms are classified as context dependent and context independent algorithms. This article discusses about context dependent algorithms QEWTSS and QEGBCPR and their performances are compared using the evaluation metrics such as Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP) metrics. The data set used is Lexical Knowledge Base (LKB), which is developed from training data and is used for evaluation process.