The Feasibility of Machine Learning for Query Answering — An Experiment in Two Domains

We present an experiment in which an information retrieval system usinga forest of decision trees was trained using Utgoff’s ITI algorithm on two test collections. The system was then compared with a conventional inverted indexing engine and found to give a superior performance. We argue that the method has the potential to be used in real applications where the task domain is homogeneous.