Named Entity Extraction is a mature task in the NLP field that has yielded numerous services gaining popularity in the Semantic Web community for extracting knowledge from web documents. These services are generally organized as pipelines, using dedicated APIs and different taxonomy for extracting, classifying and disambiguating named entities. Integrating one of these services in a particular application requires to implement an appropriate driver. Furthermore, the results of these services are not comparable due to different formats. This prevents the comparison of the performance of these services as well as their possible combination. We address this problem by proposing NERD, a framework which unifies 10 popular named entity extractors available on the web, and the NERD ontology which provides a rich set of axioms aligning the taxonomies of these tools.
NERD: A framework for unifying named entity recognition and disambiguation web extraction tools
EACL 2012, 13th Conference of the European Chapter of the Association for computational Linguistics, Demo Session, April 23-27, 2012, Avignon, France
Poster / Demo
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PERMALINK : https://www.eurecom.fr/publication/3677