An Associative Concept Dictionary for Natural Language Processing: Text Summarization and Word Sense Disambiguation

We constructed an Associative Concept Dictionary based on the results from large-scale association experiments with participants in order to develop simulation models and systems of understanding natural language. In this paper, we first briefly explain the construction of this dictionary and describe its unique features; we then show how to apply it to text summarization and word sense disambiguation. The text summarization method uses this dictionary for calculating the importance scores of sentences. A Contextual Semantic Network that includes the semantic relations and quantitative distance information among words is constructed as a model of human contextual understanding using this dictionary. We compare the quality of the summarization with that of human participants and that using conventional methods such as term frequencies. Our method shows that the quality of summarization is better than that of conventional methods. The word sense disambiguation method uses a Dynamic Contextual Network Model constructed using this dictionary. An interactive activation method on the network is used in this system as a model for the human dynamic contextual understanding to identify a word's meaning. The results show that such dynamic features are effective.