@inproceedings{bordea-etal-2012-expertise,
title = "Expertise Mining for Enterprise Content Management",
author = "Bordea, Georgeta and
Kirrane, Sabrina and
Buitelaar, Paul and
Pereira, Bianca",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Do{\u{g}}an, Mehmet U{\u{g}}ur and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/379_Paper.pdf",
pages = "3495--3498",
abstract = "Enterprise content analysis and platform configuration for enterprise content management is often carried out by external consultants that are not necessarily domain experts. In this paper, we propose a set of methods for automatic content analysis that allow users to gain a high level view of the enterprise content. Here, a main concern is the automatic identification of key stakeholders that should ideally be involved in analysis interviews. The proposed approach employs recent advances in term extraction, semantic term grounding, expert profiling and expert finding in an enterprise content management setting. Extracted terms are evaluated using human judges, while term grounding is evaluated using a manually created gold standard for the DBpedia datasource.",
}
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%0 Conference Proceedings
%T Expertise Mining for Enterprise Content Management
%A Bordea, Georgeta
%A Kirrane, Sabrina
%A Buitelaar, Paul
%A Pereira, Bianca
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Doğan, Mehmet Uğur
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)
%D 2012
%8 May
%I European Language Resources Association (ELRA)
%C Istanbul, Turkey
%F bordea-etal-2012-expertise
%X Enterprise content analysis and platform configuration for enterprise content management is often carried out by external consultants that are not necessarily domain experts. In this paper, we propose a set of methods for automatic content analysis that allow users to gain a high level view of the enterprise content. Here, a main concern is the automatic identification of key stakeholders that should ideally be involved in analysis interviews. The proposed approach employs recent advances in term extraction, semantic term grounding, expert profiling and expert finding in an enterprise content management setting. Extracted terms are evaluated using human judges, while term grounding is evaluated using a manually created gold standard for the DBpedia datasource.
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/379_Paper.pdf
%P 3495-3498
Markdown (Informal)
[Expertise Mining for Enterprise Content Management](http://www.lrec-conf.org/proceedings/lrec2012/pdf/379_Paper.pdf) (Bordea et al., LREC 2012)
ACL
- Georgeta Bordea, Sabrina Kirrane, Paul Buitelaar, and Bianca Pereira. 2012. Expertise Mining for Enterprise Content Management. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 3495–3498, Istanbul, Turkey. European Language Resources Association (ELRA).