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Don’t compare Apples to Oranges – Extending GERBIL for a fine grained NEL evaluation

Don’t compare Apples to Oranges – Extending GERBIL for a fine grained NEL evaluation

Published: 2016 September
Herausgeber: A. Fensel, A. Zaveri, S. Hellmann, T. Pellegrini
Buchtitel: SEMANTiCS 2016: Proceedings of the 12th International Conference on Semantic Systems
Seiten: 65--72
Verlag: ACM
Erscheinungsort: New York

Referierte Veröffentlichung


[[Abstract::In recent years, named entity linking (NEL) tools were pri- marily developed as general approaches, whereas today nu- merous tools are focusing on specific domains such as e.g. the mapping of persons and organizations only, or the an- notation of locations or events in microposts. However, the available benchmark datasets used for the evaluation of NEL tools do not reflect this focalizing trend. We have analyzed the evaluation process applied in the NEL benchmarking framework GERBIL [16] and its benchmark datasets. Based on these insights we extend the GERBIL framework to en- able a more fine grained evaluation and in deep analysis of the used benchmark datasets according to different em- phases. In this paper, we present the implementation of an adaptive filter for arbitrary entities as well as a system to au- tomatically measure benchmark dataset properties, such as the extent of content-related ambiguity and diversity. The implementation as well as a result visualization are inte- grated in the publicly available GERBIL framework.]]

Download: Media:2016Waitelonis_SEMANTICS2016.pdf
Weitere Informationen unter: Link
DOI Link: 10.1145/2993318.2993334


Information Service Engineering