Title: Clustering of multi-word named entity variants: Multilingual evaluation
Authors: JACQUET GUILLAUMEEHRMANN MaudSTEINBERGER Ralf
Citation: Proceedings of LREC 2014, Ninth International Conference on Language Resources and Evaluation p. 2548-2553
Publisher: European Language Resources Association
Publication Year: 2014
JRC N°: JRC85245
ISBN: 978-2-9517408-8-4
URI: http://www.lrec-conf.org/proceedings/lrec2014/index.html
http://www.lrec-conf.org/proceedings/lrec2014/pdf/468_Paper.pdf
http://publications.jrc.ec.europa.eu/repository/handle/JRC85245
Type: Articles in periodicals and books
Abstract: Multi-word entities, such as organisation names, are frequently written in many different ways. We have previously automatically identified over one million acronym pairs in 22 languages, consisting of their short form (e.g. EC) and their corresponding long forms (e.g. European Commission, European Union Commission). In order to automatically group such long form variants as belonging to the same entity, we cluster them, using bottom-up hierarchical clustering and pair-wise string similarity metrics (Ehrmann et al., 2013). In this paper, we address the issue of how to evaluate the named entity variant clusters automatically, with minimal human annotation effort. We present experiments that make use of Wikipedia redirection tables and we show that this method produces reasonable results.
JRC Directorate:Space, Security and Migration

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