Integrating methods from IR and QA for geographic information retrieval

This paper describes the participation of GIRSA at Geo- CLEF 2008, the geographic information retrieval task at CLEF. GIRSA combines information retrieval (IR) on geographically annotated data and question answering (QA) employing query decomposition. For the monolingual German experiments, several parameter settings were varied: using a single index or separate indexes for content and geographic annotation, using complex term weighting, adding location names from the topic narrative, and merging results from IR and QA, which yields the highest mean average precision (0.2608 MAP). For bilingual experiments, English and Portuguese topics were translated via the web services Applied Language Solutions, Google Translate, and Promt Online Translator. For both source languages, Google Translate seems to return the best translations. For English (Portuguese) topics, 60.2% (80.0%) of the maximum MAP for monolingual German experiments, or 0.1571 MAP (0.2085 MAP), is achieved. As a post-ocial experiment, translations of English topics were analysed with a parser. The results were employed to select the best translation for topic titles and descriptions. The corresponding retrieval experiment achieved 69.7% of the MAP of the best monolingual experiment