University of Hagen at GeoCLEF 2007: Exploring Location Indicators for Geographic Information Retrieval

Location indicators are text segments from which a geographic scope can be inferred, e.g. adjectives, demonyms (names for inhabitants of a place), geographic codes, orthographic variants, and abbreviations can be mapped to location names in one or more inferential steps. In this paper, the normalization of location indicators and treating morphology of location indicators for geographic information retrieval (GIR) within the system GIRSA (Geographic Information Retrieval by Semantic Annotation) are explored. Several retrieval experiments are performed on the German GeoCLEF 2007 data, including a baseline IR experiment on stemmed text (0.119 mean average precision, MAP). Results for this experiment are compared to results for experiments with normalized location indicators. Additionally, the latter approach was combined with an approach using semantic networks for retrieval (an extension of an experiment performed for GeoCLEF 2005). When using the topic title and description, the best performance was achieved by the combination of approaches (0.196 MAP); adding location names from the narrative part increased MAP to 0.258. Results indicate that 1) employing normalized location indicators improves MAP and increases the number of relevant documents found; 2) additional location names from the narrative increase MAP and recall, and 3) the semantic network approach has a high initial precision and even adds some relevant documents which were previously not found. For bilingual (English-German) experiments, queries were first translated into German before utilizing the translation as input to GIRSA. Performance for these experiments is generally lower, but reflect results for monolingual German. The baseline experiment (0.114 MAP) is clearly outperformed by all other experiments, achieving the best performance for a setup using title, description, and narrative (0.209 MAP).