We report on our experiments for the CLEF 2013 Entity Recogni-tion Challenge. Our approach is based on a combination of machine translation and NE tagging techniques. The Silver Standard Corpus (SSC) is used to obtain a corresponding annotated corpus in the target language. The plain text of the SSC is translated and a mapping is created between entities in the original and phrases in the translation, to which are associated the same CUIs as in the origi-nal. This produces a Bronze Standard Corpus (BSC) in the target language. A dictionary of entities is also created, which associates to each pair (entity text, semantic group) the corresponding CUIs that appeared in the SSC. The BSC is used to train a model for a Named Entity tagger. The model is used for tagging entities in sentences in the target language with the proper semantic group and the entity dictionary is used for associating CUIs to each of them.

Machine Translation for Entity Recognition across Languages in Biomedical Documents

ATTARDI, GIUSEPPE;
2013

Abstract

We report on our experiments for the CLEF 2013 Entity Recogni-tion Challenge. Our approach is based on a combination of machine translation and NE tagging techniques. The Silver Standard Corpus (SSC) is used to obtain a corresponding annotated corpus in the target language. The plain text of the SSC is translated and a mapping is created between entities in the original and phrases in the translation, to which are associated the same CUIs as in the origi-nal. This produces a Bronze Standard Corpus (BSC) in the target language. A dictionary of entities is also created, which associates to each pair (entity text, semantic group) the corresponding CUIs that appeared in the SSC. The BSC is used to train a model for a Named Entity tagger. The model is used for tagging entities in sentences in the target language with the proper semantic group and the entity dictionary is used for associating CUIs to each of them.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/238586
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