Measuring context-meaning for open class words in Hindi language

Word sense disambiguation (WSD), the task of identifying the intended sense of words has been a growing research area in the field of natural language processing. In this paper, the authors focused on word sense disambiguation for Hindi language using graph connectivity measures and Hindi WordNet[1]. To construct the graph for the sentence each sense of the ambiguous word is taken as a source node and all the paths which connects the sense to other words present in the sentence are added. The importance of nodes in the constructed graph are identified using node neighbor based measures (various centrality) and graph clustering based measures (denseness, graph randomness, edge density). The proposed method disambiguates all open class words (noun, verb, adjective, adverb) and disambiguates all the words present in the sentence simultaneously.