Automated classification of content components in technical communication

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.

Levels of intelligent information access using ontology transformations

“…Another means of performing this conversion is to use, for example, Quick Response (QR) codes, Radiofrequency identification (RFID) chips or user locations that can further automate the delivery and access processes driven by ontology transformations. Such automatic conversion processes hold great promise and can be seen as a key for intelligent and advanced methods of accessing information [19]. However, the premise for such approaches is that the relevant information or content should be semantically categorized and precisely described [2], and becomes therefore automatically retrievable, reusable, and reconfigurable [20].…”