An unsupervised learning method for associative relationships between verb phrases

This paper describes an unsupervised learning method for associative relationships between verb phrases, which is important in developing reliable Q&A systems. Consider the situation that a user gives a query "How much petrol was imported by Japan from Saudi Arabia?" to a Q&A system, but the text given to the system includes only the description "X tonnes of petrol was conveyed to Japan from Saudi Arabia." We think that the description is a good clue to find the answer for our query, "X tonnes." But there is no large-scale database that provides the associative relationship between "imported" and "conveyed." Our aim is to develop an unsupervised learning method that can obtain such an associative relationship, which we call scenario consistency. The method we are currently work- ing on uses an expectation-maximization (EM) based word-clustering algorithm, and we have evaluated the effectiveness of this method using Japanese verb phrases