As developers of a highly multilingual named entity recognition (NER) system, we face an evaluation resource bottleneck problem: we need evaluation data in many languages, the annotation should not be too time-consuming, and the evaluation results across languages should be comparable.
We solve the problem by automatically annotating the English version of a multi-parallel corpus and by projecting the annotations into all the other language versions. For the translation of English entities, we use a phrase-based statistical machine translation system as well as a lookup of known names from a multilingual name database. For the projection, we incrementally apply different methods: perfect string matching, perfect consonant signature matching and edit distance similarity.
The resulting annotated parallel corpus will be made available for reuse.
EHRMANN Maud;
TURCHI Marco;
STEINBERGER Ralf;
2012-01-26
Socoma Ltd.
JRC65739
1313-8502,
http://lml.bas.bg/~iva/ranlp2011/RANLR2011_Proceedings.PDF,
https://publications.jrc.ec.europa.eu/repository/handle/JRC65739,
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