Vilares and He (2017) propose a Bayesian
modelling approach where topics are modelled as
latent variables. The model is evaluated on debates
from the House of Commons of the UK Parliament.
This is the first novel work towards topic modelling.
Addawood et al. (2017) investigate the question
of whether opinion mining techniques can be used on
Congressional debates or not.
Venkata et al. (2018) provide a dataset for the
synopsis of Indian parliamentary debates and perform
stance classification of speeches, identifying if the
speaker is supporting the bill. Based on manual
analysis of the debates, they develop an annotation
scheme of four mutually exclusive categories to
analyse the purpose of the speeches.
Special attention has been paid to argumentation
in political discussions.
Walker et al. (2012) analyse deliberations and
debates by using the Internet Argument Corpus. The
corpus includes the posts from a website for political
debate where the debates are annotated for
argumentative markers like degrees of agreement
with previous post, cordiality, audience direction,
combativeness, assertiveness, emotionality of
argumentation, and sarcasm.
In parliamentary discourse, politicians expound
their beliefs and ideas through argumentation, and to
persuade the audience, they highlight some aspect of
an issue, which is commonly known as framing.
Naderi and Hirst (2015) examine how to identify
framing strategies in argumentative political speech.
They use a corpus of speeches from the Canadian
Parliament, and examine the statements with respect
to the position of the speaker towards the discussed
topic (Pro, Con, or No stance).
Petukhova et al. (2015) use the Information State
Update (ISU) approach to model the arguments of the
debaters and the support/attack links between them as
part of the formal representations of a participant’s
information state. They consider the identification of
claims and evidence relations to their premises as an
argument mining task. The ISU model provides
procedures for incorporating beliefs and expectations
shared between speaker and hearers in the tracking
model.
Lippi and Torroni (2016a) investigate how to
improve claim detection for argument mining, by
employing features from text and speech in
combination. They develop a machine learning
classifier and train it on an original dataset based on
the 2015 UK political elections debate.
Petukhova et al. (2017) have collected the
Metalogue Debate Corpus that includes 400
arguments from six different bilingual
(English/Greek) speakers. The corpus is used to
design a Virtual Debate Coach, in order to train young
parliamentarians on how to debate successfully.
Although it is often difficult to define clear properties
of persuasive debate, there are certain linguistic,
prosodic and body language features that correlate
with human judgments of such behaviour.
Haddadan et al. (2018) present annotation
guidelines for annotating arguments (their premises
and claims) in political debates. The dataset is taken
from the Commission on Presidential Debates
website which is an independent non-profit
corporation sponsoring U.S. presidential and vice-
presidential debates.
Menini et al. (2018) apply argumentation mining
techniques, in particular relation prediction, to study
political speeches – monologues, where there is no
direct interaction between opponents. They have
created a corpus, based on the transcription of
speeches and official declarations issued by Nixon
and Kennedy during 1960 Presidential campaign, of
argument pairs annotated with the support and attack
relations. They use a tool called OVA+ (Janier et al.
2014), an on-line interface for the manual analysis of
natural language arguments.
Many other studies have contributed to
development of formalisms and tools for analysing
arguments.
Chesňevar et al. (2006) introduce a specification
for an argument interchange format intended for
representation and exchange of data between various
argumentation tools and agent-based applications.
Reed et al. (2008) describe a written corpus of
argumentative reasoning. Arguments have been
analysed using techniques from argumentation theory
and have been marked up. The authors present
experiences with initial pilot data collection, which
raised a number of key questions that frame
challenges for argument corpora in general.
Besnard and Hunter (2014) consider a deductive
argument as a pair where the first item is a set of
premises, the second item is a claim, and the premises
entail the claim. This can be formalised by assuming
a logical language for the premises and the claim, and
logical entailment (or consequence relation) for
showing that the claim follows from the premises.
Stab and Gurevych (2014) present a novel
approach to model arguments, their components and
relations in persuasive essays in English. The
annotation scheme includes the annotation of claims
and premises as well as support and attack relations
for capturing the structure of argumentative