Using Machine Learning to Predict Temporal Orientation of Search Engines' Queries in the Temporalia Challenge.
Michele Filannino, Goran Nenadić · NTCIR · 2014
We present our approach to the NTCIR-11 Temporalia challenge, Temporal Query Intent Classication: predicting the temporal orientation (present, past, future, atemporal) of search engine user queries. We tackled the task as a machine learning classication problem. Due to the relatively small size of the training set provided, we used temporaloriented attributes specically designed to minimise the features’ sparsity. The best submitted run achieved 66.33% of accuracy, by correctly predicting the temporal orientation of 199 test instances out of 300. We discuss the results of the manual error analysis performed on the predicted classes, which sheds light on the main sources of error. Finally, we present some a-posteriori improvements to the best submitted run, which lead to a 6% improvement in terms of accuracy (72.33%).