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Context‐Sensitive Natural Language Generation: From Knowledge‐Driven to Data‐Driven Techniques
Author(s) -
Dethlefs Nina
Publication year - 2014
Publication title -
language and linguistics compass
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.619
H-Index - 44
ISSN - 1749-818X
DOI - 10.1111/lnc3.12067
Subject(s) - natural language generation , bridging (networking) , computer science , context (archaeology) , situational ethics , focus (optics) , natural (archaeology) , natural language , data driven , artificial intelligence , natural language processing , data science , psychology , history , computer network , social psychology , physics , archaeology , optics
Context‐sensitive Natural Language Generation is concerned with the automatic generation of system output that is in several ways adaptive to its target audience or the situational circumstances of its production. In this article, I will provide an overview of the most popular methods that have been applied to context‐sensitive generation. A particular focus will be on the shift from knowledge‐driven to data‐driven approaches that has been witnessed in the last decade. While this shift has offered powerful new methods for large‐scale adaptivity and flexible output generation, purely data‐driven approaches still struggle to reach the linguistic depth of their knowledge‐driven predecessors. Bridging the gap between both types of approaches is therefore an important future research direction.