A major natural language processing problem, word sense disambiguation is the task of identifying the correct sense of a polysemous word based on its context. In terms of machine learning, this can be considered as a supervised classification problem. A better alternative can be the use of semi-supervised classifiers since labeled data is usually scarce yet we can access large quantities of unlabeled textual data. We propose an improvement to Label Propagation which is a well-known transductive classification algorithm for word sense disambiguation. Our approach make use of a semantic diffusion kernel. We name this new algorithm as diffused label propagation algorithm (DILP). We evaluate our proposed algorithm with experiments utilizing various sizes of training sets of disambiguated corpora. With these experiments we try to answer the following questions: 1. Does our algorithm with semantic kernel formulation yield higher classification performance than the popular kernels? 2. Under which conditions does a kernel design perform better than others? 3. What kind of regularization methods result with better performance? Our experiments demonstrate that our approach can outperform baseline in terms of accuracy in several conditions.
