S
Simon Overell
Researcher at Imperial College London
Publications - 20
Citations - 510
Simon Overell is an academic researcher from Imperial College London. The author has contributed to research in topics: Geographic information retrieval & Image retrieval. The author has an hindex of 9, co-authored 20 publications receiving 505 citations. Previous affiliations of Simon Overell include Yahoo!.
Papers
More filters
Patent
Extracting structured knowledge from unstructured text
TL;DR: In this article, the authors present a knowledge representation system which includes a knowledge base in which knowledge is represented in a structured, machine-readable format that encodes meaning and techniques for extracting structured knowledge from unstructured text and for determining the reliability of such extracted knowledge are also described.
Journal ArticleDOI
Using co-occurrence models for placename disambiguation
Simon Overell,Stefan Rüger +1 more
TL;DR: It is shown how the inclusion of placenames in both the text and geographic parts of a query provides the maximum mean average precision and the benefits of a co‐occurrence model as a data source for the wider field of geographic information retrieval (GIR).
Proceedings ArticleDOI
Classifying tags using open content resources
TL;DR: A generic method for classifying tags using third party open content resources, such as Wikipedia and the Open Directory, using structural patterns that can be extracted from resource meta-data is presented.
Proceedings ArticleDOI
Geographic co-occurrence as a tool for gir.
Simon Overell,Stefan Rüger +1 more
TL;DR: The accuracy of the geographic co-occurrence model is quantified and theoretical bounds for the accuracy achievable when applied to placename disambiguation in free text are computed.
Proceedings Article
Identifying and grounding descriptions of places.
Simon Overell,Stefan Rüger +1 more
TL;DR: The plans to apply the co-occurrence models generated with Wikipedia to solve the problem of disambiguating place names in text using supervised learning techniques are outlined.