Integrating methods from IR and QA for geographic information retrieval
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
GikiP at GeoCLEF 2008: joining GIR and QA forces for querying Wikipedia
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Recursive question decomposition for answering complex geographic questions
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Challenges for indexing in GIR
SIGSPATIAL Special
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For the participation of GIRSA at the GeoCLEF 2007 task, two innovative features were introduced to the geographic information retrieval (GIR) system: identification and normalization of location indicators, i.e. text segments from which a geographic scope can be inferred, and the application of techniques from question answering. In an extension of a previously performed experiment, the latter approach was combined with an approach using semantic networks for geographic retrieval. When using the topic title and description, the best performance was achieved by the combination of approaches (0.196 mean average precision, MAP); adding location names from the narrative part increased MAP to 0.258. Results indicate that 1) employing normalized location indicators improves MAP significantly 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 the bilingual experiments, English queries were translated into German by the Promt machine translation web service. Performance for these experiments is generally lower. The baseline experiment (0.114 MAP) is clearly outperformed, achieving the best performance for a setup using title, description, and narrative (0.209 MAP).