Placename Disambiguation with Co-occurrence Models
This event took place on Wednesday 06 December 2006 at 11:30
Simon Overell Imperial College London, and KMi, The Open University
My talk will cover an introduction to Geographic Information Retrieval (GIR) and the advantages provided by indexing placenames as unambiguous locations. I will describe our GIR system which generates a large-scale co-occurrence model and applies this model to the problem of placename disambiguation. The data for the model is mined from Wikipedia and applied to the GeoCLEF corpus. An example of placename disambiguation could be when "London" is referred to in text, is it "London, UK" or "London, Ontario"? The motivation behind this problem is to make un-annotated data machine readable and allow users to query and browse data geographically. The talk will begin with a description of GIR, placename disambiguation techniques and the use of Wikipedia as a corpus. Then a description of my probabilistic models, using first and higher orders of co-occurrence. The talk will conclude with our findings on how Information Retrieval methods can be enhanced with Geographic
Knowledge.
This event took place on Wednesday 06 December 2006 at 11:30
My talk will cover an introduction to Geographic Information Retrieval (GIR) and the advantages provided by indexing placenames as unambiguous locations. I will describe our GIR system which generates a large-scale co-occurrence model and applies this model to the problem of placename disambiguation. The data for the model is mined from Wikipedia and applied to the GeoCLEF corpus. An example of placename disambiguation could be when "London" is referred to in text, is it "London, UK" or "London, Ontario"? The motivation behind this problem is to make un-annotated data machine readable and allow users to query and browse data geographically. The talk will begin with a description of GIR, placename disambiguation techniques and the use of Wikipedia as a corpus. Then a description of my probabilistic models, using first and higher orders of co-occurrence. The talk will conclude with our findings on how Information Retrieval methods can be enhanced with Geographic
Knowledge.
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We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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