Digital Natural Language Text Management in End-User Computing, and Methods for Improving Its Effectiveness

Dear Colleagues,

The entropy of digital texts connected to their correctness is hardly researched. It is mutually accepted that program codes—written in artificial languages—should be error-free, and developers use any available tools to lessen the errors of these texts. However, this is not the case in end-user computing and digital natural language texts of any forms. Considering spreadsheet management, errors originating in coding are covered to an extent. However, the data content is hardly analyzed, similar to the output of coding activities, included webpage development.

In general, end-user computing has flooded the world with erroneous natural language texts, primarily in the form of world-processed texts, presentations, and webpages (e-texts for short) and professional computing with erroneous texts in the form of program outputs, spreadsheet documents, and webpages. These e-texts are mainly created and modified due to the authors’ ignorance and carelessness, causing serious financial losses both in human and machine resources. Research has already revealed that these discrepancies originate in misconceptions circulating in education and the software industry, including both the creation and application of these pieces of software. Detecting errors is closely related to entropy in information theory, where erroneous texts are comparable to their properly edited version. The error rate is one measure which is in close connection to entropy. However, there are various concepts which are not or are hardly researched in natural language e-texts but can be expressed with entropy: the recognition and sources of errors, their frequencies, consequences (considering human and machine resources, teaching aspects, sunk cost fallacy, etc.), probability, the proportion of artificial and natural languages, the factor of surprise, how these parameters effect information flow, etc.

While publications have focused on the definition of the properly edited text and error classes, further research on entropy, information surprise, and validated measuring systems to detect the error rate of e-texts is missing, and we thus invite works focusing on these topics to advance this field.

Dr. Maria Csernoch
Guest Editor

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