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Homographic Ideogram Understanding Using Contextual Dynamic Network

Conventional methods for disambiguation problems have been using statistical methods with co-occurrence of words in their contexts. It seems that human-beings assign appropriate word senses to the given ambiguous word in the sentence depending on the words which followed the ambiguous word when they could not disambiguate by using the previous contextual information. In this research, Contextual Dynamic Network Model is developed using the Associative Concept Dictionary which includes semantic relations among concepts/words and the relations can be represented with quantitative distances among them. In this model, an interactive activation method is used to identify a word’s meaning on the Contextual Semantic Network where the activation values on the network are calculated using the distances. The proposed method constructs dynamically the Contextual Semantic Network according to the input words sequentially that appear in the sentence including an ambiguous word. Therefore, in this research, after the model calculates the activation values, if there is little difference between the activation values, it reconstructs the network depending on the next words in input sentence. The evaluation of proposed method showed that the accuracy rates are high when Contextual Semantic Network has high density whose node are extended using around the ambiguous word.