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Automated classification of content components in technical communication
Author(s) -
Oevermann Jan,
Ziegler Wolfgang
Publication year - 2018
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12157
Subject(s) - computer science , weighting , classifier (uml) , artificial intelligence , data mining , feature selection , machine learning , process (computing) , vector space model , information retrieval , pattern recognition (psychology) , medicine , radiology , operating system
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.