Finding the correct sense of a word is of great importance in many textual data related applications such as information retrieval, text mining and natural language processing. We have proposed one novel Word Sense Disambiguation (WSD) method according to its context. Based on collocation extraction score, the proposed method extracts three different features for each sense definition of a target word. These features create a feature vector and all the feature vectors create a sense matrix. Here, Restricted Boltzmann Machine (RBM) is used to enhance the sense matrix. Comparison of the proposed WSD method is made with current state-of-the-art systems using SENSEVAL and Sem Eval datasets. The proposed WSD method shows the practical implementation by applying on query-based text summary. For evaluation on query-based text summary, the proposed WSD method uses DUC datasets containing news-wire articles. Finally, the experimental analysis shows that our proposed WSD method performs better as compared to the current systems.