Georeferencing Textual Annotations and Tagsets with Geographical Knowledge and Language Models

This paper describes generic approaches for georeferencing multilingual textual annotations and sets of tags from metadata associated to textual or multimedia content with high precision. We present four approaches based on: 1) Geographical Knowledge, 2) Language Modelling (LM), 3) Language Modelling with Re-Ranking predictions, 4) Fusion of Geographical Knowledge predictions with the other approaches. The resources employed were the Geonames geographical gazetteer, the TFIDF and BM25 Information Retrieval algorithms, the Hiemstra Language Modelling (HLM) algorithm, stopwords lists from several languages, and an electronic English dictionary. The best results in georeferencing accuracy are achieved with the HLM Re-Ranking approach and its fusion with Geographical Knowledge. These strategies outperformed the best results in accuracy reported by the state-of-the art systems that participated at MediaEval 2010 official Placing task. Our best results achieved are 68.53% of accuracy georeferencing up to a distance of 100 Km.