University of Pittsburgh at GeoCLEF 2008: Towards Effective Geographic Information Retrieval

For GROUP2 topics, we scan each document retrieved and ranked according to Equation 1 for geographic types (geo-types) as well as determine the geo- types of geographic names found

The NER information and the geo-relation components in relevant documents, and the NER locations from the geographic query are the input for Geo- Relation Validator Subsystem

Rocha, Paulo; Di Nunzio, Giorgio; Ferro, Nicola (2007): GeoCLEF 2006: the CLEF 2006 Cross-Language Geographic Information Retrieval Track Overview. In: Peters, Carol

The engine is supported by a module that uses LingPipe 2 for HMM-based Named Entity recognition (this module performs the task of recognizing geographical names in text), and

When using the topic title and description, the best performance was achieved by the com- bination of approaches (0.196 MAP); adding location names from the narrative part increased

Geographic Ranking: As the new text mining approach generates a geographic signature for each document (D Sig ), and the geographic query expansion module generates a

For GeoCLEF 2006, twenty-five search topics were defined by the organizing groups for searching English, German, Portuguese and Spanish document collections.. Topics were

Figure 3 shows the system architecture of our GIR system used in the GeoCLEF 2006, which consists of four major modules: (1) the geographic knowledge base, which provides