=Paper=
{{Paper
|id=Vol-2048/paper11
|storemode=property
|title=Mining Bipolar Argumentation Frameworks from Natural Language Text
|pdfUrl=https://ceur-ws.org/Vol-2048/paper11.pdf
|volume=Vol-2048
|authors=Oana Cocarascu,Francesca Toni
|dblpUrl=https://dblp.org/rec/conf/icail/CocarascuT17
}}
==Mining Bipolar Argumentation Frameworks from Natural Language Text==
https://ceur-ws.org/Vol-2048/paper11.pdf
Mining Bipolar Argumentation Frameworks
from natural language text
Oana Cocarascu Francesca Toni
Imperial College London Imperial College London
oc511@imperial.ac.uk f.toni@imperial.ac.uk
ABSTRACT but not vice versa. This choice is well suited in settings such
We describe a methodology for mining topic-dependent Bipo- as online reviews, where information is provided incremen-
lar Argumentation Frameworks (BAFs) from natural lan- tally, over time, and contributors have full vision of existing
guage text. Our focus is on identifying attack and support data.
argumentative relations between texts about the same topic, We illustrate our methodology with natural language text
treating these texts as arguments when they are argumenta- drawn from a dataset of hotel reviews [17]. In this context,
tively related to other texts. We illustrate our methodology the topics are represented by aspects that users mention in
on a dataset of hotel reviews and outline some possible ap- the reviews, and BAFs represent how arguments from re-
plications using the BAFs resulting from our methodology. views relate to arguments from other reviews as well as to
arguments about the quality of the items being reviewed. For
example, consider the following two reviews about a hotel
(with the second being more recent than the first):
1 INTRODUCTION
Argument Mining is a relatively new research area which r1 : Exceeds any expectations - rooms, food, atmosphere were
involves, amongst others, the automatic detection in text heads above anywhere. Thanks for making my trip the best
of arguments, argument components, and relations between of the best.
arguments (see [15] for an overview). Argument Mining is a r2 : The room was not clean. Don’t waste your time or money
complex task because of the lack of a clear argumentative here.
structure in free natural language text. Argument Mining can
be seen as a pipeline, composed of several stages, including: For the topic room, our methodology may give the BAF
identifying argumentative sentences, detecting component graphically shown in Figure 1 (where nodes of the graph are
boundaries and argument components, and determining rela- arguments in the BAF, edges labelled by + indicate support
tions between arguments. and edges labelled by - indicate attack). Here, the (root)
In this paper, we focus on identifying argumentative rela- G (stating that the hotel is good) is supported by Groom
tions of attack and support between texts, assuming that if (stating that the rooms in this hotel are good), related to
one text attacks/supports another, then both may be consid- the topic identified. Then, ↵1 and ↵2 , drawn from reviews r1
ered to be argumentative, irrespectively of their stand-alone and r2 respectively, are about the same topic room. Here, ↵1
argumentativeness. This task, referred to as Relation-based supports Groom and ↵2 attacks ↵1 . Because of the temporal
Argument Mining (RbAM) [6], has attracted some atten- dimension underlying our methodology, ↵1 does not attack
tion lately, seen as a stand-alone classification task to be ↵2 and ↵2 does not attack Groom .
addressed by Machine Learning (ML) techniques [2, 5, 6]. As
a classification problem, RbAM can be thought of as deter- G: the hotel is good
mining a class amongst attack, support, and neither attack
nor support for any given pair of texts, to determine which +
type of argumentative relation the first element of the pair Groom : the rooms in this hotel are good
is in with the second. Note that RbAM does not rely on or
+
assume any specific argument model or internal structure of
arguments. ↵1 : rooms exceeded any expectations
We propose a methodology for mining Bipolar Argumen-
tation Frameworks (BAFs) [7] from natural language text,
↵2 : the room was not clean
with RbAM at its core. BAFs are a well known kind of ar-
gumentation framework in the literature on Argumentation
& AI [19, 21]. RbAM is well-suited for mining BAFs since Figure 1: BAF extracted from reviews r1 and r2 .
attack and support relations between (abstract) arguments
are the main components of BAFs. Our methodology relies In [9], we have shown how mining automatically BAFs
upon constructing a topic-dependent BAF from text, using from text, and reviews in particular, can be useful to support
topics to identify pairs of chunks of text to be classified using other activities (deception detection in [9]). In the current
RbAM, along a temporal dimension whereby more recent paper, we discuss some additional applications that can be
chunks of text may either support or attack less recent ones, supported once the BAFs have been extracted from text.
18th Workshop on Computational Models of Natural Argument 65
Floris Bex, Floriana Grasso, Nancy Green (eds)
16th July 2017, London, UK
The remainder of this paper is organised as follows. We No one who voted in favour of the tax increase is a desirable
present background on BAFs and related work in Section 2. candidate. Therefore, Councilwoman Radcli↵e is not a desir-
In Section 3 we outline our methodology for mining BAFs able candidate.
from natural language text and illustrate its application to an
excerpt from a dataset of reviews about a hotel. We discuss RbAM can be seen as a prerequisite for constructing BAFs. It
some applications of BAFs mined from text in Section 4 and has been traditionally treated as a ML classification problem
conclude in Section 5. We do not report any experimental with three classes: support, attack, neither support nor attack.
results for our methodology in this paper; some experiments Various approaches have been used to determine (attack/support)
are described in [9]. relations between arguments, varying from standard ML clas-
sifiers [5] to textual entailment [3, 4]. Extracting attack and
2 BACKGROUND AND RELATED support relations was also done on a corpus consisting of
WORK tweets [2]. Identifying attack and support relations between
an evaluative expression and an argument was addressed in
(Abstract) Argumentation Frameworks (AAFs) are pairs con-
[12], on a French corpus covering domains such as hotels,
sisting of a set of arguments and a binary relation between
restaurants, and politics.
arguments, representing attacks [11]. Formally, an AAF is any
hAR, attacksi where attacks ✓ AR ⇥ AR. Bipolar Argumen-
tation Frameworks (BAFs) extend AAFs by considering two 3 A METHODOLOGY FOR MINING
independent binary relations between arguments: attack and BAFS
support [7]. Formally, a BAF is any hAR, attacks, supportsi RbAM is a difficult task which amounts to identifying the
where hAR, attacksi is an AAF and supports ✓ AR ⇥ AR. pieces of text between which there may be an argumentative
For example, consider the following three texts: relation as well as the type of relation between these pieces of
text. When dealing with large texts, e.g. drawn from online
t1 : ‘We should grant politicians immunity from prosecution’ product reviews or online debates, analysing every possible
t2 : ‘Giving politicians immunity from prosecution allows them pair of pieces of texts in order to determine relations is simply
to focus on performing their duties’ not feasible.
t3 : ‘The ability to prosecute politicians is the ultimate protec- We propose a methodology to extract BAFs from natural
tion against the abuse of power’ language text where the arguments that form the BAFs are
clustered based on the topics extracted from the text being
Here t2 supports t1 and t3 attacks t1 . Thus, these three texts analysed. In order to construct BAFs, we use a temporal
can be seen as the arguments in the BAF represented as the dimension to decide which texts to compare to determine the
following graph (where nodes are arguments, edges labelled - relation as well as to determine the type of relation between
indicate attacks and edges labelled + indicate supports): texts (i.e. support, attack, or neither). Thus, we construct
+ topic-dependent BAFs by determining the relations between
t2 t1 t3 arguments that refer to the same topic, following the temporal
Argument Mining is an emerging field whose aims include order, in such a way that more recent texts can relate to
to identify argumentative sentences, argument components less recent ones, but not vice versa. The rationale behind
and argument structures (such as claims and premises), as the topic-oriented approach is that arguments that mention
well as to identify relations between arguments (such as sup- di↵erent topics are highly unlikely to be related (i.e. neither
port and attack) (see [15] for a recent overview). Argument argument supports nor attacks the other argument). The
Mining mostly relies on Natural Language Processing (NLP) rationale behind the reliance on a temporal ordering is that
and Machine Learning (ML) techniques. In this paper we it allows to limit the number of relations in BAFs. Our
focus on Relation-based Argument Mining (RbAM) [6], a illustration demonstrates that this approach is useful and
subtask in Argument Mining which aims to automatically allows to generate BAFs that can be easily understood.
identify argumentative relations between texts, of the kinds Our procedure for constructing a BAF from text is as
(attack and support) occurring in BAFs. In RbAM, if one follows:
text attacks/supports another, then both may be considered (1) split the text into temporally ordered sentences; we
to be argumentative, irrespectively of their stand-alone argu- thus assume that each argument extracted from the
mentativeness. For example, consider the following sentence: reviews is contained in a sentence, and that each
sentence contains one or more potential argument;
Councilwoman Radcli↵e voted in favour of the tax increase. (2) identify topics in texts and, for each topic, the sen-
tences (potential arguments) related to the topic;
Analysed in isolation, this sentence does not seem to be (3) for each topic, for each pair of sentences related
argumentative but becomes an argument when read in con- to that topic, determine whether the most recent
text: sentence supports, attacks, or neither supports nor
attacks the less recent sentence; compare a sentence
Councilwoman Radcli↵e voted in favour of the tax increase. with its (temporally) closest less recent sentence first,
2
66 18th Workshop on Computational Models of Natural Argument
Floris Bex, Floriana Grasso, Nancy Green (eds)
16th July 2017, London, UK
and compare the sentence with less recent sentences a pre-trained tokenizer for English. Sentences containing
than its closest ones only if it neither supports nor specific keywords such as but, although, though, otherwise,
attacks the closest ones; however, unless,whereas, are split since, in general, the phrases
(4) construct the BAF. before and after these separators express di↵erent sentiments
In the remainder of this section, we illustrate our method- (e.g. ‘The sta↵ was nice but the room was messy’ results in
ology when applied to the reviews in Table 1, which represent two sentences with di↵erent sentiments).
an excerpt from a dataset consisting of positive and negative At this step, in preparation for steps 3 and 4, we also de-
hotel (deceptive or truthful) reviews [17]. In the illustration, termine the sentiment polarity of sentences. This polarity can
we will sometimes refer to the implementation given in [9] of be identified using a lexicon of frequently used adjectives in
our proposed methodology. product reviews annotated with scores for sentiment polarity
[10].
R1 This hotel is absolutely beautiful. Our room was gorgeous. I
As an illustration of this first step, from R1 , we identify
do have 1 complaint. The bar is very boring and the restaurant is the following pieces of texts containing potential arguments,
not that great, but other than that I loved it. I would definitely with polarity (+ for positive and - for negative sentiment) as
stay there again. indicated:
R2 Very pleasant front sta↵, large rooms, and free Internet for
those that are members of the loyalty program. room was gorgeous (+)
R3 Sta↵ were helpful & friendly, room was huge with fantastic the bar is very boring and the restaurant is not that great (-)
view of the river. Adjoining Bar/Restaurant, great food.
R4 I stayed here last August and I’m truly glad that I will whereas from R10 we identify the following potential ar-
never have to stay here again. The website does a great job gument, with polarity as indicated:
of creating an illusion. The rooms are so much smaller than
it seems on the website. The wireless internet is free but it is
located in a hard to find location, the restaurant is uncom-
extremely slow. All and all, I would not recommend this hotel
to anyone. fortably
R5 It is one of the nicest hotels i have stayed at in my life, crowded, the sta↵ is hard to reach (-)1
clean, comfortable and pretty. The rooms were clean and the
sta↵ is very caring. We will see next that some potential arguments can be split
R6 I have stayed in hotels all over the world, and this is probably further.
the worst that I’ve ever experienced. The sta↵ was unaccom-
modating, the front desk sta↵ was condescending and not even 3.2 Illustration of Step 2
remotely helpful. The room was not clean. Don’t waste your
In building a topic-dependent BAF from a set of reviews, we
time or your money here.
R7 The sta↵ are polite and well poised. The rooms, hallways
first identify ‘topics’ mentioned in the reviews.
and facilities were exceptionally clean and tidy. During my Various approaches for identifying topics in text exist, rang-
stay, I stopped at their restaurant where I had one of the best ing from associating each noun encountered in texts to a topic,
American style meal in a while. Overall, this hotel is a place I to more advanced techniques related to topic modeling such
would surely stay at again if given the chance to visit Chicago as Latent Dirichlet Allocation (LDA) [1] and Non-negative
for a second time. It is truly exceptional. Matrix Factorization (NMF) [14], able to uncover the under-
R8 I loved the location and the amenities o↵ered by this hotel. lying semantic structure of text by identifying topics and the
The room was charming with a window seat and a water view. words that belong to topics.
Free wireless internet were a plus here. The sta↵ was helpful For our reviews, if we associate the nouns encountered in at
and attentive. I would definitely stay here again.
least a few reviews to a topic, then we identify the following
R9 There was only one person from the sta↵ at the front desk
topics: sta↵, room, internet, bar, restaurant, location. The
when we arrived, preoccupied with something on their computer
so our presence was not acknowledged for several minutes. Then
intuition here is that, in the case of online reviews, if a topic is
when we got to our room, I found it to be incredibly dusty. controversial/debatable, then it will be mentioned by at least
Overall it was a good stay but those two inconveniences made a few users (either to support the argument given initially or
us question the amount of money we paid for it. to attack it).
R10 The hotel is located in a hard to find location in Chicago, We then identify the sentences/arguments related to these
the restaurant is uncomfortably crowded, the sta↵ is hard to topics. In the case of topics being associated to nouns, we
reach, overall it was not a pleasant hotel stay. extract the sentences that contain these specific nouns. For
Table 1: Reviews for a hotel in Chicago. LDA/NMF, we extract the sentences containing any of the
top words associated to the extracted topics.
For example, from R1 , we identify the following arguments,
with polarity and topics as indicated:
3.1 Illustration of Step 1 1
Note that we use components of argumentative sentences to stand for
the full sentences. For example, the first part of the latter potential
The first step in constructing BAFs is to split the texts argument stands for “The hotel is located in a hard to find location
analysed into sentences. This can be done for example with in Chicago”.
3
18th Workshop on Computational Models of Natural Argument 67
Floris Bex, Floriana Grasso, Nancy Green (eds)
16th July 2017, London, UK
Topic Argument Argument
room: a1,1 : room was gorgeous (+) id
bar : a1,2 : the bar is very boring (-) a2,1 very pleasant front sta↵
restaurant: a1,3 : the restaurant is not that great (-) a3,1 sta↵ were helpful & friendly
a5,2 the sta↵ is very caring
whereas from R10 we identify: a6,1 the sta↵ was unaccommodating,
sta↵
the front desk sta↵ was conde-
location: a10,1 : located in a hard to find location (-) scending and not even remotely
restaurant: a10,2 : the restaurant is uncomfortably crowded (-) helpful
sta↵ : a10, : the sta↵ is hard to reach (-) 2 a7,1 the sta↵ are polite and well
poised
3.3 Illustration of Step 3 a8,4 the sta↵ was helpful and atten-
tive
Determining relations between sentences/arguments in any
a9,1 there was only one person from
pair can be viewed as a three-class problem, with classi-
the sta↵ at the front desk when
fication labels {attack, support, neither}. For this step, we
we arrived, preoccupied with
can use ML classifiers to determine relations between sen-
something on their computer so
tences/arguments associated to topics identified in the previ-
our presence was not acknowl-
ous step. In [9] we used Random Forests for classifying the
edged for several minutes
type of relation between arguments identified at step 2, using
a10,3 the sta↵ is hard to reach
the arguments’ sentiment polarity as a feature.
a1,1 room was gorgeous
a2,2 large rooms
3.4 Illustration of Step 4 a3,2 room was huge with fantastic
The arguments in the BAF include a single special argument view
G (for ‘good’) as well as a special argument Gt per topic t, room a4,1 the rooms are so much smaller
as already seen in Section 1. Gt stands for ‘good as far as t than it seems on the website
is concerned’, such that each Gt supports G. a5,1 the rooms were clean
We use a temporal approach for determining the relations a6,2 the room was not clean
between arguments related to topic t drawn from reviews a7,2 the rooms were exceptionally
and the special argument Gt . In particular, we assume that clean and tidy
a newer argument (with respect to time) can either support, a8,2 the room was charming with a
attack, or neither support nor attack a previous argument or window seat and a water view
Gt , but not vice versa. Note that the use of this temporal a9,2 our room, I found it to be incred-
‘filter’ is well suited in the context of online reviews, but it ibly dusty.
might not be applicable in other settings. If an argument a2,3 free Internet for those that are
at , related to topic t, does not support or attack another internet members of the loyalty program
argument related to t from the same or some other review, as a4,2 the wireless internet is extremely
determined by RbAM at step 3, then at will either support slow
or attack Gt , according to its polarity. a8,3 free wireless internet were a plus
Figure 2 shows the BAF extracted from the reviews in here
Table 1. The extracted arguments occurring as nodes in a1,2 the bar is very boring
the BAF are shown in Table 2. Note that not all text in bar
a3,3 adjoining Bar, great food
the reviews contributes to the arguments in the BAF. For a1,3 the restaurant is not that great
example, the first sentence from R1 : “This hotel is absolutely a3,4 adjoining Restaurant, great food
beautiful ” does not represent an argument as it mentions restaurant
a7,3 at their restaurant where I had
‘hotel’ and not a specific topic related to hotel. one of the best American style
As an illustration, consider the first three arguments about meal in a while
the topic room: a1,1 , a2,2 , a3,2 . As a1,1 is the first argument a10,2 the restaurant is uncomfortably
that mentions the topic, it is connected to Groom according crowded
to its polarity (i.e. a1,1 supports Groom ). In this case step a8,1 I loved the location
3 deemed that a2,2 neither attacks nor supports a1,1 (we location a10,1 located in a hard to find location
only consider relations from a2,2 to a1,1 as per our temporal
Table 2: Arguments extracted from the reviews in
approach). Then a2,2 supports Groom as it has a positive
Table 1.
polarity and there is no other less recent argument to be
compared with. Here step 3 also identifies a support relation
2
In our notation, ax,y represents the yth argument from review x .
4
68 18th Workshop on Computational Models of Natural Argument
Floris Bex, Floriana Grasso, Nancy Green (eds)
16th July 2017, London, UK
G example in Section 3, the strengths for our Gt arguments are:
strength(Gsta↵ ) = 0.955078 strength(Groom ) = 0.967773
+ + strength(Ginternet ) = 0.6875 strength(Gbar ) = 0.375
+ strength(Grestaurant ) = 0.40625 strength(Glocation ) = 0.625
+ + +
These provide a measure of how good the hotel is, along the
various dimensions (topics) considered, according to the avail-
Gsta↵ Groom Ginternet Gbar Grestaurant Glocation
+ + able reviews, and can also be used to compare the hotel with
+ + + + others.
+
a2,1 a3,1 a1,1 a2,2 a5,1 a2,3 a1,2 a1,3 a8,1 Further, BAFs could be employed in tasks such as sum-
marisation as they provide a structured and concise view of
+ + + +
the aspects (topics) mentioned in text. In our example hotel,
a7,1 a5,2 a8,2 a3,2 a6,2 a4,2 a3,3 a3,4 a10,1 from the BAF in Figure 2, we could e.g. hypothesise that
+ internet used to be good, since the first review mentioning
internet was positive (a2,3 ), but has since been unstable,
a6,1 a4,1 a7,2 a8,3 a7,3
as the next review mentioning it is negative (a4,2 ) and is
followed by a positive review (see a8,3 , which attacks a4,2 ).
a8,4 a9,2 a10,2 BAFs can also help in identifying arguments that are
widely accepted as well as identifying conflicting viewpoints
that arise in debates. Consider the following simple example:
a9,1
+ room was not too good
+ +
a10,3
the room was not pretty at allthe room was not very clean
Figure 2: BAF obtained from the reviews in Table 1.
Here the reviews lead to the conclusion that the rooms
were not good in the particular hotel under consideration,
and the root argument in the graph is widely accepted.
between a3,2 and a2,2 . Thus this relation is included in the We leave the exploration of these and additional applica-
BAF. tions of BAFs extracted by means of our methodology for
Now consider argument a3,2 . While this can be deemed to future work.
support both a2,2 and Groom , it only supports the latter in the
BAF in Figure 2. Indeed, in our temporal approach we first 5 CONCLUSION
check a3,2 against a2,2 . If a relation is found between these We proposed a methodology for mining Bipolar Argumenta-
arguments, then we do not check for the relation between tion Frameworks (BAFs) from natural language text, relying
a3,2 and Groom as we want a “minimal” BAF, in terms of on Relation-based Argument Mining (RbAM), a standard
the number of relations it accommodates. Similarly, for a4,1 , classification problem in NLP, to identify argumentative rela-
we check for a relation between this argument and the most tions between sentences, seen as arguments by virtue of being
recent one, in this case a3,2 . If step 3 had not identified any in argumentative relations. In particular, our methodology
relation between these two arguments, then a4,1 would have uses RbAM to construct BAFs by determining relations be-
been checked against a2,2 , the next “related” argument. tween texts that refer to the same topic, along a temporal
dimension whereby more recent texts may either support
4 SOME APPLICATIONS or attack less recent ones, but not vice versa. We have il-
In [9] we have used the BAFs extracted from reviews to iden- lustrated our methodology on hotel reviews and discussed
tify (argumentative) features to be fed to ML classifiers for the usefulness of our approach in application settings such
detecting deception e↵ectively. The BAFs provide semantic as online user comments (reviews and debates) where ar-
information on top of the syntactic features obtained through guments lack a clear structure or have incomplete/missing
standard NLP techniques. justifications. These applications for BAFs mined from text
The BAFs obtained from natural language text using our may help extract information and go well beyond the narrow
methodology can be used for other purposes too. For exam- classification task underlying standard RbAM.
ple, if applied to online settings such as debates and reviews, This paper gives a pilot investigation, by hand, of our
various notions of dialectical strength or acceptability of ar- proposed methodology. We have referred, in our illustrations,
guments in AAFs and BAFs may be deployed to evaluate the to an implementation of our methodology [9], that also gives
outcomes of the debates or reviews, as suggested in [8]. For experimental results. Much future work is needed to explore
illustration, using the DF-QuAD method [18], that quantifies other implementations and applicability in the settings we
the strength of arguments by aggregating the strength of considered and beyond, supported by experimentation. We
their attackers and supporters, in the case of the reviews also plan to test whether the temporal dimension is useful in
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18th Workshop on Computational Models of Natural Argument 69
Floris Bex, Floriana Grasso, Nancy Green (eds)
16th July 2017, London, UK
other settings, di↵erent from online reviews. We have focused [18] Antonio Rago, Francesca Toni, Marco Aurisicchio, and Pietro Ba-
on extracting BAFs from text. Other works extract di↵erent roni. 2016. Discontinuity-Free Decision Support with Quantitative
Argumentation Debates. In Principles of Knowledge Representa-
types of argument graphs (e.g. [20]), for other application tion and Reasoning: Proceedings of the Fifteenth International
areas (e.g parliamentary debates [20]). We plan to test and/or Conference, KR. AAAI Press, 63–73.
[19] Iyad Rahwan and Guillermo R. Simari. 2009. Argumentation in
adapt our approach for this and other settings. Finally, future Artificial Intelligence (1st ed.). Springer Publishing Company,
work also includes experimenting whether first determining Incorporated.
arguments based on their argumentative structure, e.g. as in [20] Zaher Salah, Frans Coenen, and Davide Grossi. 2013. Extracting
Debate Graphs from Parliamentary Transcripts: A Study Directed
[13, 16], may be useful to single out chunks of text to be fed at UK House of Commons Debates. In Proceedings of the Four-
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[21] Frans H. van Eemeren, Bart Garssen, Erik C. W. Krabbe, A. Fran-
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Floris Bex, Floriana Grasso, Nancy Green (eds)
16th July 2017, London, UK