@inproceedings{drissi-etal-2008-development,
title = "A Development Environment for Configurable Meta-Annotators in a Pipelined {NLP} Architecture",
author = "Drissi, Youssef and
Boguraev, Branimir and
Ferrucci, David and
Keyser, Paul and
Levas, Anthony",
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/820_paper.pdf",
abstract = "Information extraction from large data repositories is critical to Information Management solutions. In addition to prerequisite corpus analysis, to determine domain-specific characteristics of text resources, developing, refining and evaluating analytics entails a complex and lengthy process, typically requiring more than just domain expertise. Modern architectures for text processing, while facilitating reuse and (re-)composition of analytical pipelines, do place additional constraints upon the analytics development, as domain experts need not only configure individual annotator components, but situate these within a fully functional annotator pipeline. We present the design, and current status, of a tool for configuring model-driven annotators, which abstracts away from annotator implementation details, pipeline composition constraints, and data management. Instead, the tool embodies support for all stages of ontology-centric model development cycle from corpus analysis and concept definition, to model development and testing, to large scale evaluation, to easy and rapid composition of text applications deploying these concept models. With our design, we aim to meet the needs of domain experts, who are not necessarily expert NLP practitioners.",
}
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<abstract>Information extraction from large data repositories is critical to Information Management solutions. In addition to prerequisite corpus analysis, to determine domain-specific characteristics of text resources, developing, refining and evaluating analytics entails a complex and lengthy process, typically requiring more than just domain expertise. Modern architectures for text processing, while facilitating reuse and (re-)composition of analytical pipelines, do place additional constraints upon the analytics development, as domain experts need not only configure individual annotator components, but situate these within a fully functional annotator pipeline. We present the design, and current status, of a tool for configuring model-driven annotators, which abstracts away from annotator implementation details, pipeline composition constraints, and data management. Instead, the tool embodies support for all stages of ontology-centric model development cycle from corpus analysis and concept definition, to model development and testing, to large scale evaluation, to easy and rapid composition of text applications deploying these concept models. With our design, we aim to meet the needs of domain experts, who are not necessarily expert NLP practitioners.</abstract>
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%0 Conference Proceedings
%T A Development Environment for Configurable Meta-Annotators in a Pipelined NLP Architecture
%A Drissi, Youssef
%A Boguraev, Branimir
%A Ferrucci, David
%A Keyser, Paul
%A Levas, Anthony
%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 drissi-etal-2008-development
%X Information extraction from large data repositories is critical to Information Management solutions. In addition to prerequisite corpus analysis, to determine domain-specific characteristics of text resources, developing, refining and evaluating analytics entails a complex and lengthy process, typically requiring more than just domain expertise. Modern architectures for text processing, while facilitating reuse and (re-)composition of analytical pipelines, do place additional constraints upon the analytics development, as domain experts need not only configure individual annotator components, but situate these within a fully functional annotator pipeline. We present the design, and current status, of a tool for configuring model-driven annotators, which abstracts away from annotator implementation details, pipeline composition constraints, and data management. Instead, the tool embodies support for all stages of ontology-centric model development cycle from corpus analysis and concept definition, to model development and testing, to large scale evaluation, to easy and rapid composition of text applications deploying these concept models. With our design, we aim to meet the needs of domain experts, who are not necessarily expert NLP practitioners.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/820_paper.pdf
Markdown (Informal)
[A Development Environment for Configurable Meta-Annotators in a Pipelined NLP Architecture](http://www.lrec-conf.org/proceedings/lrec2008/pdf/820_paper.pdf) (Drissi et al., LREC 2008)
ACL