@inproceedings{scerri-etal-2010-classifying,
title = "Classifying Action Items for Semantic Email",
author = "Scerri, Simon and
Gossen, Gerhard and
Davis, Brian and
Handschuh, Siegfried",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Rosner, Mike and
Tapias, Daniel",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/39_Paper.pdf",
abstract = "Email can be considered as a virtual working environment in which users are constantly struggling to manage the vast amount of exchanged data. Although most of this data belongs to well-defined workflows, these are implicit and largely unsupported by existing email clients. Semanta provides this support by enabling Semantic Email ― email enhanced with machine-processable metadata about specific types of email Action Items (e.g. Task Assignment, Meeting Proposal). In the larger picture, these items form part of ad-hoc workflows (e.g. Task Delegation, Meeting Scheduling). Semanta is faced with a knowledge-acquisition bottleneck, as users cannot be expected to annotate each action item, and their automatic recognition proves difficult. This paper focuses on applying computationally treatable aspects of speech act theory for the classification of email action items. A rule-based classification model is employed, based on the presence or form of a number of linguistic features. The technologys evaluation suggests that whereas full automation is not feasible, the results are good enough to be presented as suggestions for the user to review. In addition the rule-based system will bootstrap a machine learning system that is currently in development, to generate the initial training sets which are then improved through the users reviewing.",
}
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<abstract>Email can be considered as a virtual working environment in which users are constantly struggling to manage the vast amount of exchanged data. Although most of this data belongs to well-defined workflows, these are implicit and largely unsupported by existing email clients. Semanta provides this support by enabling Semantic Email ― email enhanced with machine-processable metadata about specific types of email Action Items (e.g. Task Assignment, Meeting Proposal). In the larger picture, these items form part of ad-hoc workflows (e.g. Task Delegation, Meeting Scheduling). Semanta is faced with a knowledge-acquisition bottleneck, as users cannot be expected to annotate each action item, and their automatic recognition proves difficult. This paper focuses on applying computationally treatable aspects of speech act theory for the classification of email action items. A rule-based classification model is employed, based on the presence or form of a number of linguistic features. The technologys evaluation suggests that whereas full automation is not feasible, the results are good enough to be presented as suggestions for the user to review. In addition the rule-based system will bootstrap a machine learning system that is currently in development, to generate the initial training sets which are then improved through the users reviewing.</abstract>
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%0 Conference Proceedings
%T Classifying Action Items for Semantic Email
%A Scerri, Simon
%A Gossen, Gerhard
%A Davis, Brian
%A Handschuh, Siegfried
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 May
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F scerri-etal-2010-classifying
%X Email can be considered as a virtual working environment in which users are constantly struggling to manage the vast amount of exchanged data. Although most of this data belongs to well-defined workflows, these are implicit and largely unsupported by existing email clients. Semanta provides this support by enabling Semantic Email ― email enhanced with machine-processable metadata about specific types of email Action Items (e.g. Task Assignment, Meeting Proposal). In the larger picture, these items form part of ad-hoc workflows (e.g. Task Delegation, Meeting Scheduling). Semanta is faced with a knowledge-acquisition bottleneck, as users cannot be expected to annotate each action item, and their automatic recognition proves difficult. This paper focuses on applying computationally treatable aspects of speech act theory for the classification of email action items. A rule-based classification model is employed, based on the presence or form of a number of linguistic features. The technologys evaluation suggests that whereas full automation is not feasible, the results are good enough to be presented as suggestions for the user to review. In addition the rule-based system will bootstrap a machine learning system that is currently in development, to generate the initial training sets which are then improved through the users reviewing.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/39_Paper.pdf
Markdown (Informal)
[Classifying Action Items for Semantic Email](http://www.lrec-conf.org/proceedings/lrec2010/pdf/39_Paper.pdf) (Scerri et al., LREC 2010)
ACL
- Simon Scerri, Gerhard Gossen, Brian Davis, and Siegfried Handschuh. 2010. Classifying Action Items for Semantic Email. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).