Les Entit\'es Nomm\'ees : usage et degr\'es de pr\'ecision et de d\'esambigu\"isation

TL;DR

This paper explores how using extended syntactic context and large-scale resources can improve Named Entity Recognition (NER) accuracy in NLP applications, especially when immediate context is insufficient.

Contribution

It introduces the use of extended syntactic context and large-scale resources to enhance NER classification accuracy beyond immediate context limitations.

Findings

Extended syntactic context improves NER classification.

Large-scale resources enhance disambiguation.

Method outperforms traditional immediate context approaches.

Abstract

The recognition and classification of Named Entities (NER) are regarded as an important component for many Natural Language Processing (NLP) applications. The classification is usually made by taking into account the immediate context in which the NE appears. In some cases, this immediate context does not allow getting the right classification. We show in this paper that the use of an extended syntactic context and large-scale resources could be very useful in the NER task.