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    <abstract>Semantic Web and NLP We describe an implemented offline procedure that maps OWL/RDF-encoded ontologies with large, dynamically maintained instance data to named entity recognition (NER) and information extraction (IE) engine resources, preserving hierarchical concept information and links back to the ontology concepts and instances. The main motivations are (i) improving NER/IE precision and recall in closed domains, (ii) exploiting linguistic knowledge (context, inflection, anaphora) for identifying ontology instances in texts more robustly, (iii) giving full access to ontology instances and concepts in natural language processing results, e.g. for subsequent ontology queries, navigation or inference, (iv) avoiding duplication of work in development and maintenance of similar resources in independent places, namely lingware and ontologies. We show an application in hybrid deep-shallow natural language processing that is e.g. used for question analysis in closed domains. Further applications could be automatic hyperlinking or other innovative semantic-web related applications.</abstract>
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%0 Conference Proceedings
%T OntoNERdIE – Mapping and Linking Ontologies to Named Entity Recognition and Information Extraction Resources
%A Schäfer, Ulrich
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Gangemi, Aldo
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Tapias, Daniel
%S Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
%D 2006
%8 May
%I European Language Resources Association (ELRA)
%C Genoa, Italy
%F schafer-2006-ontonerdie
%X Semantic Web and NLP We describe an implemented offline procedure that maps OWL/RDF-encoded ontologies with large, dynamically maintained instance data to named entity recognition (NER) and information extraction (IE) engine resources, preserving hierarchical concept information and links back to the ontology concepts and instances. The main motivations are (i) improving NER/IE precision and recall in closed domains, (ii) exploiting linguistic knowledge (context, inflection, anaphora) for identifying ontology instances in texts more robustly, (iii) giving full access to ontology instances and concepts in natural language processing results, e.g. for subsequent ontology queries, navigation or inference, (iv) avoiding duplication of work in development and maintenance of similar resources in independent places, namely lingware and ontologies. We show an application in hybrid deep-shallow natural language processing that is e.g. used for question analysis in closed domains. Further applications could be automatic hyperlinking or other innovative semantic-web related applications.
%U http://www.lrec-conf.org/proceedings/lrec2006/pdf/196_pdf.pdf

Markdown (Informal)

[OntoNERdIE – Mapping and Linking Ontologies to Named Entity Recognition and Information Extraction Resources](http://www.lrec-conf.org/proceedings/lrec2006/pdf/196_pdf.pdf) (Schäfer, LREC 2006)

• OntoNERdIE – Mapping and Linking Ontologies to Named Entity Recognition and Information Extraction Resources (Schäfer, LREC 2006)

ACL

• Ulrich Schäfer. 2006. OntoNERdIE – Mapping and Linking Ontologies to Named Entity Recognition and Information Extraction Resources. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).