Summary of the paper
| Title | Clustering of Multi-Word Named Entity Variants: Multilingual Evaluation |
|---|---|
| Authors | Guillaume Jacquet, Maud Ehrmann and Ralf Steinberger |
| 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. 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 good results. |
| Topics | Named Entity Recognition, Multilinguality |
Full paper  |
Clustering of Multi-Word Named Entity Variants: Multilingual Evaluation |
| Bibtex | @InProceedings{JACQUET14.468, author = {Guillaume Jacquet and Maud Ehrmann and Ralf Steinberger}, title = {Clustering of Multi-Word Named Entity Variants: Multilingual Evaluation}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |