@inproceedings{davis-etal-2008-linguistically,
title = "Linguistically Light Lexical Extensions for Ontologies",
author = "Davis, Brian and
Handschuh, Siegfried and
Troussov, Alexander and
Judge, John and
Sogrin, Mikhail",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Tapias, Daniel",
booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)",
month = may,
year = "2008",
address = "Marrakech, Morocco",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/911_paper.pdf",
abstract = "The identification of class instances within unstructured text for either the purposes of Ontology population or semantic annotation are usually limited to term mentions of Proper Noun and Personal Noun or fixed Key Phrases within Text Analytics or Ontology based Information Extraction(OBIE) applications. These systems do not generalize to cope with compound nominal classes of multi word expressions. Computational Linguistics approaches involving deep analysis tend to suffer from idiomaticity and overgeneration problems while the shallower words with spaces approach frequently employed in Information Extraction(IE) and Industrial Text Analytics systems lacks flexibility and is prone to lexical proliferation. We outline a representation for encoding light linguistic features of Compound Nominal term mentions of Concepts within an Ontology as well as a lightweight semantic annotator which complies the above linguistic information into efficient Dictionary formats to drive large scale identification and semantic annotation of the aforementioned concepts.",
}
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<abstract>The identification of class instances within unstructured text for either the purposes of Ontology population or semantic annotation are usually limited to term mentions of Proper Noun and Personal Noun or fixed Key Phrases within Text Analytics or Ontology based Information Extraction(OBIE) applications. These systems do not generalize to cope with compound nominal classes of multi word expressions. Computational Linguistics approaches involving deep analysis tend to suffer from idiomaticity and overgeneration problems while the shallower words with spaces approach frequently employed in Information Extraction(IE) and Industrial Text Analytics systems lacks flexibility and is prone to lexical proliferation. We outline a representation for encoding light linguistic features of Compound Nominal term mentions of Concepts within an Ontology as well as a lightweight semantic annotator which complies the above linguistic information into efficient Dictionary formats to drive large scale identification and semantic annotation of the aforementioned concepts.</abstract>
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%0 Conference Proceedings
%T Linguistically Light Lexical Extensions for Ontologies
%A Davis, Brian
%A Handschuh, Siegfried
%A Troussov, Alexander
%A Judge, John
%A Sogrin, Mikhail
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Tapias, Daniel
%S Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)
%D 2008
%8 May
%I European Language Resources Association (ELRA)
%C Marrakech, Morocco
%F davis-etal-2008-linguistically
%X The identification of class instances within unstructured text for either the purposes of Ontology population or semantic annotation are usually limited to term mentions of Proper Noun and Personal Noun or fixed Key Phrases within Text Analytics or Ontology based Information Extraction(OBIE) applications. These systems do not generalize to cope with compound nominal classes of multi word expressions. Computational Linguistics approaches involving deep analysis tend to suffer from idiomaticity and overgeneration problems while the shallower words with spaces approach frequently employed in Information Extraction(IE) and Industrial Text Analytics systems lacks flexibility and is prone to lexical proliferation. We outline a representation for encoding light linguistic features of Compound Nominal term mentions of Concepts within an Ontology as well as a lightweight semantic annotator which complies the above linguistic information into efficient Dictionary formats to drive large scale identification and semantic annotation of the aforementioned concepts.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/911_paper.pdf
Markdown (Informal)
[Linguistically Light Lexical Extensions for Ontologies](http://www.lrec-conf.org/proceedings/lrec2008/pdf/911_paper.pdf) (Davis et al., LREC 2008)
ACL
- Brian Davis, Siegfried Handschuh, Alexander Troussov, John Judge, and Mikhail Sogrin. 2008. Linguistically Light Lexical Extensions for Ontologies. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).