About this Research Topic
Context is a slippery customer. It has been vaguely defined as the parts of discourse surrounding a word, sentence, or passage, the set of situational elements that includes the object being processed or what surrounds and gives meaning to something else. Nevertheless, despite the difficulty of pinning down context, all of us are extremely sensitive to it. We are intuitively aware of which features of context make our utterances meaningful. We are also adept at contextualizing what we read or hear in order to understand it. Yet, the specification and representation of context remain elusive, as reflected in the disparity and scarcity of contextual information in general and specialized dictionaries, termbases, etc. Nonetheless, the systematic inclusion of contextual data would greatly benefit these resources, especially those related to Natural Language Processing (NLP) and domain ontologies. This would significantly enrich entries, facilitate knowledge acquisition, and also provide computers with a greater capacity to interpret context-specific data.
Even though researchers acknowledge the importance of context, there is no consensus on how it should be specified or even extracted. This is thus a challenge that should be addressed since both general and specialized concepts (along with their designations) are more easily identified and understood when they are represented in context. Specific proposals for context representation in dictionaries and knowledge bases include frames; contextonyms; semantic relations and networks; and context-aware ontologies, among others. In fact, to date, there is no cohesive body of literature that can serve as a reference for knowledge resource designers. Even though the inclusion of context is a priority in such resources, more empirical and practical research is needed to discover how this can be systematically accomplished on a larger scale.
The scope of this article collection encompasses the design of knowledge resources and the inclusion of contextual information from the perspective of Lexicography and Terminography. Research on different types of context is relevant as well as optimal ways of representing context types. Also of interest are the measurement, specification, and visualization of semantic relations and semantic relatedness. Additional topics are the semi-automatic or automatic extraction of contextual data from corpora as well as the configuration of this information to highlight knowledge-rich contexts. Research topics include but are not limited to the following:
• Representation of contextual information in knowledge resources;
• Use of contextual information for knowledge representation (disambiguation, conceptual and semantic relations, etc.);
• Contextual variation and specification;
• Context analysis, identification, and extraction;
• Context selection and relevance.
Even though researchers acknowledge the importance of context, there is no consensus on how it should be specified or even extracted. This is thus a challenge that should be addressed since both general and specialized concepts (along with their designations) are more easily identified and understood when they are represented in context. Specific proposals for context representation in dictionaries and knowledge bases include frames; contextonyms; semantic relations and networks; and context-aware ontologies, among others. In fact, to date, there is no cohesive body of literature that can serve as a reference for knowledge resource designers. Even though the inclusion of context is a priority in such resources, more empirical and practical research is needed to discover how this can be systematically accomplished on a larger scale.
The scope of this article collection encompasses the design of knowledge resources and the inclusion of contextual information from the perspective of Lexicography and Terminography. Research on different types of context is relevant as well as optimal ways of representing context types. Also of interest are the measurement, specification, and visualization of semantic relations and semantic relatedness. Additional topics are the semi-automatic or automatic extraction of contextual data from corpora as well as the configuration of this information to highlight knowledge-rich contexts. Research topics include but are not limited to the following:
• Representation of contextual information in knowledge resources;
• Use of contextual information for knowledge representation (disambiguation, conceptual and semantic relations, etc.);
• Contextual variation and specification;
• Context analysis, identification, and extraction;
• Context selection and relevance.
Keywords: context representation, contextual information, contextual variation, contextual specification, context analysis, context identification, context extraction, context selection, context relevance
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