Graph Based Computational Model for Computing Semantic Similarity

Finding semantic similarity between two natural language entities considered a challenging task in the field of natural language processing. Accuracy of presently existing semantic similarity computational methods is still very far from what humans would perceive. In this paper, we present a new approach of measuring semantic similarity/distance between concepts/words by considering all senses instead of using one most common sense of concepts in WordNet hierarchy. Our proposed approach considers not only the semantic distance between two concepts/words but also considers feature information of WordNet graph. When tested on benchmark data set of words pair similarity ratings, the proposed approach performs better than other semantic similarity computational models for ambiguous words/concepts (which has more than one sense). Proposed approach gives the highest correlation coefficient value with human similarity judgments based benchmark data set.