Automated classification of content components in technical communication

Automated classification of content components in technical communication Automated classification is usually not adjusted to specialized domains due to a lack of suitable data collections and insufficient characterization of the domain‐specific content and its effect on the classification process. This work describes an approach for the automated multiclass classification of content components used in technical communication based on a vector space model. We show that differences in the form and substance of content components require an adaption of document‐based classification methods and validate our assumptions with multiple real‐world data sets in 2 languages. As a result, we propose general adaptions on feature selection and token weighting, as well as new ideas for the measurement of classifier confidence and the semantic weighting of XML‐based training data. We introduce several potential applications of our method and provide prototypical implementation. Our contribution beyond the state of the art is a dedicated procedure model for the automated classification of content components in technical communication, which outperforms current document‐centered or domain‐agnostic approaches. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Intelligence Wiley

Automated classification of content components in technical communication

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018 Wiley Periodicals, Inc.
ISSN
0824-7935
eISSN
1467-8640
D.O.I.
10.1111/coin.12157
Publisher site
See Article on Publisher Site

Abstract

Automated classification is usually not adjusted to specialized domains due to a lack of suitable data collections and insufficient characterization of the domain‐specific content and its effect on the classification process. This work describes an approach for the automated multiclass classification of content components used in technical communication based on a vector space model. We show that differences in the form and substance of content components require an adaption of document‐based classification methods and validate our assumptions with multiple real‐world data sets in 2 languages. As a result, we propose general adaptions on feature selection and token weighting, as well as new ideas for the measurement of classifier confidence and the semantic weighting of XML‐based training data. We introduce several potential applications of our method and provide prototypical implementation. Our contribution beyond the state of the art is a dedicated procedure model for the automated classification of content components in technical communication, which outperforms current document‐centered or domain‐agnostic approaches.

Journal

Computational IntelligenceWiley

Published: Jan 1, 2018

Keywords: ; ; ;

References

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