An Analysis of the Performances of the CasEN Named Entities Recognition System in the Ester2 Evaluation CampaignReport as inadecuate




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1 BDTLN - Bases de données et traitement des langues naturelles LI - Laboratoire d-Informatique de l-Université de Tours

Abstract : In this paper, we present a detailed and critical analysis of the behaviour of the CasEN named entity recognition system during the French Ester2 evaluation campaign. In this project, CasEN has been confronted with the task of detecting and categorizing named entities in manual and automatic transcriptions of radio broadcastings. At first, we give a general presentation of the Ester2 campaign. Then, we describe our system, based on transducers. Next, we depict how systems were evaluated during this campaign and we report the main official results. Afterwards, we investigate in details the influence of some annotation biases which have significantly affected the estimation of the performances of systems. At last, we conduct an in-depth analysis of the effective errors of the CasEN system, providing us with some useful indications about phenomena that gave rise to errors e.g. metonymy, encapsulation, detection of right boundaries and are as many challenges for named entity recognition systems.

Keywords : sequences mining evaluation text mining Named Entity detection





Author: Damien Nouvel - Jean-Yves Antoine - Nathalie Friburger - Denis Maurel -

Source: https://hal.archives-ouvertes.fr/



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