Information and Communication Technologies 1 Overview of GeoCLEF 2007 IR techniques IE/NLP techniques GIR techniques Systems Resources Experiments Translation.

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Presentation transcript:

Information and Communication Technologies 1 Overview of GeoCLEF 2007 IR techniques IE/NLP techniques GIR techniques Systems Resources Experiments Translation General comments

Information and Communication Technologies 2 IR techniques and systems automatic and manual query expansion blind relevance feedback INL2 term weighting model divergence from randomness framework latent Dirichlet allocation model logistic regression query decomposition vector space model stemming systems: Lemur (2), Lucene (4), MG4J, Terrier (2), Zebra DBMS

Information and Communication Technologies 3 NLP techniques and systems named entity recognition WordNet-based expansion semantic analysis part-of-speech tagging use of a QA system for subqueries decompounding (for German) WordNet lemmatizer systems: LingPipe, Annie (GATE)

Information and Communication Technologies 4 GIR/GIE techniques location disambiguation geographic unique strings location normalization query expansion based on geographical terms query expansion based on a geographic ontology heuristic geographically informed filtering removing candidates using shape files for close or near geographical relations separate geographical indexes geographic cooccurrence model (based on Wikipedia) geographic relation finder

Information and Communication Technologies 5 GIR/GIE resources geonames gazetteer World Gazetteer Getty TGN GNIS GeoWorldMap World gazetteer ADL Feature Type thesaurus GKB 2.0 Spanish toponymy list plus Wikipedia and WordNet

Information and Communication Technologies 6 Translation (only queries) Systran (PT-EN) Prompt (ES-EN) Promt (EN-DE) LEC Power Translator (all possible pairs) transfer MT system (ES-EN, ES-PT) Toggletext (ID-EN)

Information and Communication Technologies 7 Experiments In addition to the particular experiments of each group concerning their original approaches Separate indexes for geographic and non-geographic information Apply different techniques to different parts of the query Use T, TD or TDN

Information and Communication Technologies 8 Comments Very few papers mention distinguished treatment for different kinds of topics although some discuss differences between topics only one provides some per-topic analysis Most participants have best results with text only! Most participants use NER, but no NER evaluation in itself has been reported: does it really help? or does it also diminish performance? they don’t use the output of NER the same way: wide difference in behaviour after NER has been invoked