University of Hagen at GeoCLEF2006: Experiments with Metonymy Recognition in Documents

This paper describes the participation of the IICS group at the GeoCLEF task of the CLEF campaign 2006. We describe different retrieval experiments using a separate index for location names and identifying and indexing of metonymic location names differently. The setup of our GIR system is a modified variant of the setup for GeoCLEF 2005. We apply a classifier for the identification of metonymic location names for preprocessing the documents. This classifier is based on shallow features only and was trained on manually annotated data from the German CoNLL-2003 Shared Task corpus for Language-Independent Named Entity Recognition and from a subset of the GeoCLEF newspaper corpus. After preprocessing, documents contain additional information for location names that are to be indexed separately, i.e. LOC (all location names identified), LOCLIT (location names in their literal sense), and LOCMET (location names in their metonymic sense). To obtain an IR query from the topic title, description, and narrative, we employ two methods. In the first method, a semantic parser analyzes the query text and the resulting semantic net is transformed into database query. The second method uses a Boolean combination of a bag-of-words (consisting of topical search terms) with location names. The results of our experiments can be summarized as follows: excluding metonymic senses of location names improves mean average precision (MAP) for most of our experiments. For experiments in which this was not the case, a more detailed analysis showed that for some topics the precision increased. Our experiments show that the additional use of topic narratives decreases MAP. For almost all experiments with the topic narrative, lower values for MAP and for the number of relevant and retrieved documents were observed. However, query expansion and the use of separate indexes improves the performance of our GIR application.