Application of String Vector based K Nearest Neighbor to Semantic Word Classification

This article proposes the modified KNN (K Nearest Neighbor) algorithm which receives a string vector as its input data and is applied to the word categorization. The results from applying the string vector based algorithms to the text categorizations were successful in previous works and synergy effect between the text categorization and the word categorization is expected by combiningthem with each other; the two facts become motivations for this research. In this research, we define the operation on string vectors called semantic similarity, modify the KNN algorithm by replacing the exiting similarity metric by the proposed one, and apply it to the word categorization. The proposed KNN is empirically validated as the better approach in categorizing words in newsarticles and opinions. We need to define and characterize mathematically more operations on string vectors for modifying more advanced machine learning algorithms.