Abstract
Context encompasses the classification of a certain environment by its key attributes that take the role of semantic markers. It is an abstract representation of a certain data environment. In texts, the context classifies and represents a piece of text in a generalized form. Context can be a recursive construct when summarizing information on a more coarse-grained level. This paper presents identification and standardization of context on different levels of granularity that finally supports faster and more precise information retrieval. The prototypical system presented here applies supervised learning for a semi-automatic approach to extract, distil, and standardize data from text. The approach is based on named-entity recognition and simple ontologies for identification and disambiguation of context. Even though the prototype shown here still represents work in progress, it already demonstrates its potential for mining texts on different levels of context granularity. The paper presents the design of the Contexter system that supports identification and classification of misinformation and fake news around the topic Covid-19.
Keywords: Information Extraction, Named-Entity Recognition, Bag of Words, Information Summarization, Entity Linking, Semantic Markers, Context-Awareness, Economic Information
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