Abstract
The lexical ambiguity in word sense disambiguation is a crucial problem in the fields of machine translation and information retrieval. The lexical ambiguity causes because of polysemy words in a natural language understanding. The polysemy words may confuse the machine while processing user inputs. In machine translation, the polysemy words are processed with the help of available context information. The objective of this research work is to present a supervised deep neural network model dedicated to the task of maximizing the accuracy. The input layer of the neural network will consist of nodes having binary values depending on the presence or absence of frequently occurring context words related to the ambiguous words. The output layer will consist of nodes equal to the number of senses the ambiguous word has. Training and testing of the model will be done using lexical resources such as SemCor or OMSTI. The accuracy will be calculated based on All-Word tasks from SemEval international workshops. The purpose of this research is to improve better communication between man and machine.
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