Tree Representations in Probabilistic Models for Extended Named Entities Detection.
EACL '12: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics(2012)
摘要
In this paper we deal with Named Entity Recognition (NER) on transcriptions of French broadcast data. Two aspects make the task more difficult with respect to previous NER tasks: i) named entities annotated used in this work have a tree structure, thus the task cannot be tackled as a sequence labelling task; ii) the data used are more noisy than data used for previous NER tasks. We approach the task in two steps, involving Conditional Random Fields and Probabilistic Context-Free Grammars, integrated in a single parsing algorithm. We analyse the effect of using several tree representations. Our system outperforms the best system of the evaluation campaign by a significant margin.
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关键词
previous NER task,French broadcast data,best system,tree representation,tree structure,Conditional Random Fields,Entity Recognition,Probabilistic Context-Free Grammars,evaluation campaign,significant margin,entities detection,probabilistic model
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