Argumentation mining is an application of natural language
processing (NLP) that emerged a few years ago and has recently
enjoyed considerable popularity, as demonstrated by a series of
international workshops and by a rising number of publications at
the major conferences and journals of the field. Its goals are to
identify argumentation in text or dialogue; to construct
representations of the constellation of claims, supporting and
attacking moves (in different levels of detail); and to
characterize the patterns of reasoning that appear to license the
argumentation. Furthermore, recent work also addresses the
difficult tasks of evaluating the persuasiveness and quality of
arguments. Some of the linguistic genres that are being studied
include legal text, student essays, political discourse and debate,
newspaper editorials, scientific writing, and others. The book
starts with a discussion of the linguistic perspective,
characteristics of argumentative language, and their relationship
to certain other notions such as subjectivity. Besides the
connection to linguistics, argumentation has for a long time been a
topic in Artificial Intelligence, where the focus is on devising
adequate representations and reasoning formalisms that capture the
properties of argumentative exchange. It is generally very
difficult to connect the two realms of reasoning and text analysis,
but we are convinced that it should be attempted in the long term,
and therefore we also touch upon some fundamentals of reasoning
approaches. Then the book turns to its focus, the computational
side of mining argumentation in text. We first introduce a number
of annotated corpora that have been used in the research. From the
NLP perspective, argumentation mining shares subtasks with research
fields such as subjectivity and sentiment analysis, semantic
relation extraction, and discourse parsing. Therefore, many
technical approaches are being borrowed from those (and other)
fields. We break argumentation mining into a series of subtasks,
starting with the preparatory steps of classifying text as
argumentative (or not) and segmenting it into elementary units.
Then, central steps are the automatic identification of claims, and
finding statements that support or oppose the claim. For certain
applications, it is also of interest to compute a full structure of
an argumentative constellation of statements. Next, we discuss a
few steps that try to 'dig deeper': to infer the underlying
reasoning pattern for a textual argument, to reconstruct unstated
premises (so-called 'enthymemes'), and to evaluate the quality of
the argumentation. We also take a brief look at 'the other side' of
mining, i.e., the generation or synthesis of argumentative text.
The book finishes with a summary of the argumentation mining tasks,
a sketch of potential applications, and a--necessarily
subjective--outlook for the field.
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