Annotation for computational argumentation analysis: Issues and perspectives

Abstract

AbstractArgumentation has long been studied in a number of disciplines, including several branches of linguistics. In recent years, computational processing of argumentation has been added to the list, reflecting a general interest from the field of natural language processing (NLP) in building natural language understanding systems for increasingly intricate language phenomena. Computational argumentation analysis – referred to as argumentation mining in the NLP literature – requires large amounts of real‐world text with manually analyzed argumentation. This process is known as annotation in the NLP literature and such annotated datasets are used both as “gold standards” for assessing the quality of NLP applications and as training data for the machine learning algorithms underlying most state of the art approaches to NLP. Argumentation annotation turns out to be complex, both because argumentation can be complex in itself and because it does not come across as a unitary phenomenon in the literature. In this survey we review how argumentation has been studied in other fields, how it has been annotated in NLP and what has been achieved so far. We conclude with describing some important current and future issues to be resolved.