Abstract: In NLP, the main two fundamental tasks are Word Sense Disambiguation (WSD) and Semantic Role Labeling (SRL). Semantic role labeling is used to label the arguments in the sentence with the help of predicates. Word sense disambiguation is the procedure of predicting the correct definition of a word in a given context. In the research, we improved SRL using WSD and dependency parsing. The dependency parser helps to improve the semantic relationship between the predicates and its arguments. A modified Conditional Random Field (CRF) is used to bind dependency parser with SRL. We have used SVM classifier for WSD and PractNLP tool is used for dependency parser. The model is evaluated and compared with an online WSD with SRL tool. From the results obtained with the aid of our proposals, the labeling performs much better than a tool.