learning to rank for geographic information retrieval
The task of Learning to Rank is currently getting increasing attention, providind a sound methodology for combining different sources of evidence. The goal is to design and apply machine learning methods to automatically learn a function from training data that can sort documents according to their relevance. Geographic information retrieval has also emerged as an active and growing research area, addressing the retrieval of textual documents according to geographic criteria of relevance. In this paper, we explore the usage of a learning to rank approach for geographic information retrieval, leveraging on the datasets made available in the context of the previous GeoCLEF evaluation campaigns. The idea is to combine different metrics of textual and geographic similarity into a single ranking function, through the use of the SV Mmap framework. Experimental results show that the proposed approach can outperform baselines based on heuristic combinations of features.
learning to rank geographic information retrieval