=Paper=
{{Paper
|id=Vol-2048/paper02
|storemode=property
|title=Argumentation Mining in Scientific Discourse
|pdfUrl=https://ceur-ws.org/Vol-2048/paper02.pdf
|volume=Vol-2048
|authors=Nancy L. Green
|dblpUrl=https://dblp.org/rec/conf/icail/Green17
}}
==Argumentation Mining in Scientific Discourse==
https://ceur-ws.org/Vol-2048/paper02.pdf
Argumentation Mining in Scientific Discourse
Nancy L. Green
University of North Carolina Greensboro, Greensboro, NC, USA
nlgreen@uncg.edu
Abstract audience, as well as constraints of the underlying
argumentation scheme [Green, 2010].
The dominant approach to argumentation
mining has been to treat argumentation Although human-level understanding of natural
scheme detection as a machine learning language text is currently beyond the state of the art,
problem based upon superficial text features, we contend that an inference-based approach is
and to treat the relationships between feasible for applications requiring a deeper analysis
arguments as support or attack. However, of argumentation. In [Green, 2016] we proposed an
applications such as accurately representing approach to mining individual arguments in
and summarizing argumentation in scientific biomedical research articles using argumentation
research articles require a deeper schemes implemented as logic programs. The
understanding of the text and a richer model schemes are formulated in terms of semantic
of relationships between arguments. This predicates that could be obtained from a text by use
paper presents a semantic rule-based of BioNLP (biomedical/biological natural language
approach to extracting individual arguments, processing) tools. This semantic approach to mining
and demonstrates the need for a richer avoids the various problems faced by purely feature-
model of inter-argument relationships in based approaches, e.g., that argument components
biomedical/biological research articles. may be conveyed through non-contiguous or
overlapping text segments of varying granularity, the
sparcity of discourse cues marking argument
1 Introduction components, and the occurrence of enthymemes.
The dominant approach to argumentation (or In this paper, we build on our previous proposal by
argument) mining [e.g., Green et al., 2014; Cardie et considering the role of discourse structure in mining
al., 2015; Reed et al., 2016] has been to treat it as a argumentation in scientific texts. In section 2 we
machine learning problem based upon superficial text summarize our previous proposal to mining
features, enabling researchers to adopt methods that individual arguments. In section 3 we discuss the
have been applied successfully to other natural relationship of the individual arguments to other
language processing tasks. This approach has been aspects of discourse structure, and how the arguments
useful for applications such as identifying reasons are related to each other, i.e., the argumentation
given for opinions in social media, or automatic structure of the discourse.
assessment of student essay quality. However some
applications, such as accurately summarizing 2 Mining Arguments
argumentation in scientific research articles, require a This section summarizes our proposed approach to
deeper understanding of the text. mining individual arguments described in [Green,
There are a number of problems with mining 2016], using argumentation schemes implemented as
arguments in scientific documents at the text level logic programs written in Prolog [Bratko, 2001].
rather than at the semantic level [Green, 2015a; Argumentation schemes are abstract descriptions of
2015b]. Argument components may not occur in acceptable, possibly defeasible, arguments used in
contiguity. In fact, the content of an argument may be conversation as well as in formal genres such as legal
widely separated or the content of two arguments and scientific text [Walton et al., 2008]. To provide
may be interleaved at the text level. Furthermore examples of argumentation schemes in open-access
scientific text often contains enthymemes, i.e. text, we analyzed arguments in the Results section of
arguments with implicit premises or an implicit a biomedical research article [van de Leemput et al.,
conclusion. Interpretation of enthymemes may 2007] in the CRAFT corpus [CRAFT]. The CRAFT
require use of the preceding discourse context corpus has been annotated by other researchers for
(including inferred conclusions of other arguments), purposes of biomedical text mining [Verspoor et al.,
presumed shared knowledge of the author and 2012; Bada et al., 2012], but not for argument
mining. The seven argumentation schemes presented
18th Workshop on Computational Models of Natural Argument 7
Floris Bex, Floriana Grasso, Nancy Green (eds)
16th July 2017, London, UK
in [Green, 2016] were implemented in terms of To use the implemented rules for argument
domain-specific semantic predicates that could in mining, i.e., to extract individual arguments, it is
theory be automatically extracted by BioNLP tools. assumed that, firstly, BioNLP tools would be applied
(Results of a preliminary study of human analysts’ to a source text to create a knowledge base (KB).
ability to apply the argumentation schemes Named entity recognition tools such as ABNER
consistently will be reported in the future.) We expect [Settles, 2005] or MutationFinder [Caporaso et al.,
that these rules, while domain-specific, are applicable 2007] could be used to recognize expressions
to the large body of research articles on genetic referring to semantic class names such as genes,
variants with effects on human health. mutations, proteins, and phenotypes. Domain-
To illustrate a few of the schemes, first, the specific relations in the argumentation schemes such
Method of Agreement scheme can be paraphrased as as have_phenotype and have_genotype could be
follows. extracted from the text using relation extraction tools
such as OpenMutationMinder [Naderi and Witte,
Premises: 2012] and DiMeX [Mahmood et al., 2016]. Also, a
• A group of individuals G have atypical certain amount of domain knowledge would be
phenotype P required, e.g., for the relations similar and difference,
• All of the individuals in G have atypical which could be acquired from a domain ontology or
genotype M. domain experts. After a KB has been created, the
Conclusion: M may be the cause of P (in G). argument scheme rules would be applied to the KB to
recognize the premises, conclusion, and
In the rules, genotype describes a variation (mutation) argumentation scheme of each argument in the text.
at the level of chromosome, region on the The rules were tested by manually creating a KB and
chromosome, or gene that may have a deleterious then applying the rules to the KB.
effect (or effects), and phenotype describes the To illustrate, the implementation of the
effect(s). This scheme can be seen as a specialization argumentation scheme for Method of Agreement is as
of a more general scheme, related to Mill’s Method follows.
of Agreement [Jenicek and Hitchcock, 2005].
Another scheme, related to Mill’s Method of arg(
Difference [Jenicek and Hitchcock, 2005], can be scheme('Agreement'),
paraphrased as follows. premise(have_phenotype(G, P)),
premise(have_genotype(G, M)),
Premises: conclusion(cause(M, P)))
• A group of individuals G have atypical :-
phenotype P group(G),
• All of the individuals in G have atypical have_phenotype(G, P),
genotype M. have_genotype(G, M).
• A group of individuals Control do not have
P. Applying this rule to a KB containing the facts:
• None of the individuals in Control have M.
Conclusion: M may be the cause of P (in G). group(mice1).
have_phenotype(mice1, ataxia).
The following scheme can be seen as a specialization have_genotype(mice1, ‘Itpr1 opt/opt’).
of Argument by Analogy, e.g. as described in [Walton
et al., 2008]. would derive an argument whose scheme is identified
as Agreement, whose premises are the above listed
Premises: facts, and whose conclusion is that the Itpr1 opt/opt
• Phenotype P1 of group G1 is similar to variant may be the cause of their ataxia. Note that
phenotype P2 of group G2 the rules are formulated in such a way that even
• Genotype M1 of group G1 may be the cause implicit conclusions of arguments can be recognized,
of P1. given the premises and an argumentation scheme
• Genotype M2 of group G2 is similar to rule. The inferred conclusion itself can be added to
genotype M1. the KB, enabling it to be used as a premise in
Conclusion: M2 may be the cause of P2 (in G2). subsequent arguments. Also note that the conclusions
of the schemes are not asserted with complete
certainty. The corresponding arguments in the source
8 18th Workshop on Computational Models of Natural Argument
Floris Bex, Floriana Grasso, Nancy Green (eds)
16th July 2017, London, UK
text range in force from ‘plausible hypothesis’ to of an experiment, e.g. Conclusion, will be
‘fairly certain conclusion’. For details of the Prolog capitalized, and labels of argument components will
implementation of the seven schemes, see [Green, be italicized.)
2016]. In the report of Experiment 1, the conclusion of
This approach is in contrast to machine-learning Argument 1 must be inferred to understand the
approaches to argumentation scheme recognition that Conclusion of the experiment. Also, it can be seen
use only superficial text features such as keywords, that the conclusion of Argument 1 is a premise
parts of speech, and clause length [Feng and Hirst, needed in the argument (Argument 2) for the
2011; Lawrence and Reed, 2016]. In addition, some Hypothesis of Experiment 2 in the next paragraph.
of those approaches, e.g. [Feng and Hirst], assume Note that Experiment 2 contains two arguments, one
that clauses of a text are labeled as premise or (Argument 2) for the Hypothesis, and one (Argument
conclusion before argumentation scheme recognition 3) for the Conclusion. The conclusions of Argument
begins. On the other hand, similar to our approach, 2 and Argument 3 are not identical, i.e., the
[Saint-Dizier, 2012] uses manually-derived rules conclusion of Argument 3 is more specific than that
encoded in a logic programming language for of Argument 2. The conclusion of Argument 3 can
automatic identification of arguments giving reasons be challenged by a critical question of the Method of
for a conclusion in instructional texts or opinion Agreement scheme, i.e., whether the putative cause
texts. However, the rules are based on syntactic of the disorder is causally plausible. Experiment 3
patterns and lexical features. contains Argument 4, whose conclusion addresses
that critical question. Note that, in general, critical
3 The Results Narrative questions associated with argumentation schemes
provide ways in which arguments may be challenged
Having proposed an approach to mining individual
[Walton et al., 2008].
arguments, the next step of our research is to
Paragraph 4 states a new Goal: to discover any
investigate how the arguments are related to other
related genetic variants causing a similar disorder in
aspects of discourse structure, and how the arguments
humans. The article then goes on to describe another
are related to each other. Previous computation-
sequence of experiments towards that goal.
oriented investigations of discourse in the natural
In Experiment 4, Background Knowledge and the
sciences have addressed automatic classification of
conclusion of Argument 4 (in Experiment 3) are used
text segments, e.g., discourse coherence relations in
(in Argument 5) to argue for the Hypothesis of the
corpora such as BioDRB [Prasad et al., 2011] and
experiment. Based on the Result of Experiment 4,
BioCause [Mihaila et al., 2013], argumentative zones
the implicit Conclusion (conclusion of Argument 6)
[Teufel, 2010], and activities in a scientific
that a deletion in ITPR1-SUMF1 may be the cause, is
investigation (CoreSC) [Liakata, 2012]. None of
broader than the original Hypothesis, that a deletion
those annotation schemes treat arguments in the sense
in ITPR1 may be the cause. However, the conclusion
described in the previous section.
is also narrower in the sense that it is restricted to
The Results section of the article whose arguments
particular individuals in the AUS1 family.
were analyzed in [Green, 2016] reports on a logical
In Experiments 5 and 6, the respective conclusions
and temporal sequence of experiments. Arguments
of Argument 7 and Argument 8 agree with the
are given in the context of this narrative, i.e. the
conclusion of Argument 6. Experiment 7 provides
report of the scientific investigation. Figure 1 shows
support (Argument 9) for a related conclusion. The
our ad hoc analysis of the narrative using descriptive
conclusions of arguments in Experiments 5-7 are
terms similar to those of the argumentative zone and
used as premises to argue for a more general
CoreSC systems. The relevant content of the article
conclusion in Argument 10. Then, using results of
has been paraphrased in the figure.
Previous Research, the authors argue (Argument 11)
The Results section begins, in its first paragraph,
against part of the conclusion of Argument 10. The
with a description of the fortuitous discovery of an
conclusion of Argument 11 is a premise of Argument
inherited disorder in mice bred in the authors’ lab.
12, whose conclusion is a refinement of the
Then the authors describe a sequence of three
conclusion of Argument 10. Finally, the conclusion
experiments intended to reveal the genetic variant
of Argument 13 addresses a critical question of
responsible for that mouse disorder. Figure 1
Argument 12, a relationship like that of Argument 4
describes each individual experiment in terms of its
to Argument 3.
Goal, (use of) Previous Research or Background
As for the first of the above research questions
Knowledge, Hypothesis, Method, Result, and/or
(how arguments are related to other aspects of
Conclusion. (To avoid confusion with argument
discourse structure), the above analysis raises some
components, the first letter of terms describing parts
interesting possibilities. It could be that some
18th Workshop on Computational Models of Natural Argument 9
Floris Bex, Floriana Grasso, Nancy Green (eds)
16th July 2017, London, UK
argumentation schemes are more commonly used for 2001] has a key feature in common with the scientific
certain scientific purposes and not others. For article that we analyzed. In a discovery dialogue, the
example, Argument by Analogy is used only in goal is not to try to prove or disprove a given claim,
arguments for Hypotheses in the article that was but to discover something not previously known.
analyzed. This could be verified by statistical
analyses of corpora, and if true, could augment the 4 Discussion
semantic method we have proposed for extracting
individual arguments. Another possibility is that the Previous argumentation mining research has not
location in the narrative could be used as a constraint addressed the natural sciences. However,
on argument scheme recognition. Although it can be argumentation is an important feature of scientific
seen in Figure 1 that argument content is not in one- discourse. This paper proposes a semantic approach
to-one correspondence with paragraph or Experiment to automatic recognition of premises, conclusion, and
boundaries, in cases where multiple rules match, a argumentation scheme of arguments in scientific text.
heuristic strategy of preferring local content might be Argumentation schemes are implemented as logic
applied. programs. The logic programs would be used with a
As for the second of the above research questions knowledge base that could be constructed from a text
(how the arguments are related to each other), the in a large part automatically using existing language
analysis in Figure 1 shows that a richer model is processing tools (as described in section 2). The logic
needed than support-attack relationships represented programs can be used, not only to recognize fully
in current argument mining approaches, e.g., [Cabrio explicit arguments in the text, but also arguments
and Villata, 2012], [Peldszus and Stede 2016], [Stab with implicit conclusions. This is important because
and Gurevych, 2014]. The relationships between often the conclusions are implicit and may function
conclusions of the arguments in Figure 1 is as implicit premises of subsequent arguments in the
summarized in Table 1. text. Although the argumentation schemes have been
implemented using domain-specific predicates, they
Table 1. Dialectical structure of Results section. are specializations of more general schemes
Conclusion of Relationship to Conclusion of applicable to other qualitative causal domains in the
argument # argument # natural sciences.
2 Refines 1 Also, as a step towards automatic recognition of
3 Refines 2 the structure of argumentation in scientific discourse,
4 Responds to CQ 3 we present a discourse analysis of part of a scientific
of text and discuss the relationship of the individual
5 Analogous to 3 arguments to other aspects of the discourse structure,
6 Broadens from 5 and how the arguments are related to each other, i.e.,
del. of ITPR1 to the argumentation structure of the discourse. It is
ITPR1-SUMF1 shown that a richer model is needed than support-
& restricts from attack relationships.
humans to
AUS1 Acknowledgments
7 Agrees with 6 The analysis of argument schemes was done with the
8 Agrees with 6 help of graduate students, Michael Branon and
9 Extends to more 6 Bishwa Giri, who were supported by a University of
individuals North Carolina Greenboro 2016 Summer Faculty
10 Extends 9 and 6 Excellence Research Grant.
11 Attacks part of 6
and extends to References
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Appendix. Figure 1. Analysis of discourse structure and argumentation.
¶1
Report Observation: Some mice bred in the authors’ lab are affected with an autosomal recessive disorder
resembling a kind of ataxia. (premise, Argument 1)
¶2
Report Experiment 1:
Method: Linkage analysis
Result: The affected mice have a lesion on chromosome 6qE1 (premise, Argument 1)
Conclusion (implicit): A genetic variant on 6qE1 may be the cause of their disorder. (conclusion,
Argument 1 - Method of Agreement; premise, Argument 2)
¶3
Report Experiment 2:
Previous Research: A certain deletion on 6qE1 in gene Itpr1, the Itpr1 opt/opt variant, is known
to cause a similar disorder in mice. (premise, Argument 2)
Hypothesis: A deletion in Itpr1 may be the cause the disorder in the lab’s mice. (conclusion, Argument 2
– Analogy)
Method: Sequence Itpr1
Result: The lab’s affected mice have a deletion in Itpr1: the Itpr1 Δ18/Δ18 variant. (premise, Argument 3)
Conclusion: The Itpr1 Δ18/Δ18 variant may be the cause of their disorder. (conclusion, Argument
3 – Method of Agreement)
Report Experiment 3:
Previous Research: Cerebellar Purkinjee cells of Itpr1 opt/opt mice, who have ataxia, have decreased Itpr1
expression (premise, Argument 4)
Method: Measure expression of Itpr1 in cerebellar Purkinjee cells of mice with the Itpr1 Δ18/Δ18 variant.
Result: Decreased level of Itrpr1 expression found. (premise, Argument 4)
Conclusion (implicit): There is a plausible explanation at the molecular level of how deletions in
Itpr1 may cause ataxia-like disorders in mice. (conclusion, Argument 4 – Consistent Explanation;
premise, Argument 5)
¶4
Report Goal: Discover cause of cognate human disorders, such as spinocerebellar ataxia 15 (SCA15), where no
causal mutation has been identified.
12 18th Workshop on Computational Models of Natural Argument
Floris Bex, Floriana Grasso, Nancy Green (eds)
16th July 2017, London, UK
¶5
Report Experiment 4:
Background knowledge (implicit): The ITPR1 gene in humans is functionally similar to Itpr1 in
mice. (premise, Argument 5)
Hypothesis: A deletion in ITPR1 is a cause of SCA15 in humans. (conclusion, Argument 5 – Analogy)
Method: Sequence DNA from three AUS1 family members with SCA15.
Result: The three family members had a deletion in ITPR1-SUMF1. (premise, Argument 6; premise,
Argument 7)
Conclusion (implicit): A deletion in ITPR1-SUMF1 may be the cause of ataxia in the three AUS1
family members. (conclusion, Argument 6 – Method of Agreement)
Report Experiment 5:
Goal: Determine if the ITPR1-SUMF1 deletion is a benign polymorphism.
Method: Compare to ITPR1 and SUMF1 in two control groups.
Results: No deletion found in ITPR1 or SUMF1 in the control groups. (premise, Argument 7)
Conclusion: A deletion in ITPR1-SUMF1 may be the cause of ataxia in the three affected AUS1
family members. (conclusion, Argument 7 – Method of Difference)
¶6
Report Experiment 6:
Method: Fine-map the breakpoints of the deletion in the affected AUS1 family members and in the
controls.
Result: Deletion of the first three of the nine exons of SUMF1 and the first 10 of the 58 exons of
ITPR1 in the affected family members only. (premise, Argument 8)
Conclusion: A deletion in ITPR1-SUMF1 may be the cause of ataxia in the three AUS1 family members.
(conclusion, Argument 8 – Method of Difference)
¶7
Report Experiment 7:
Method: Analyzed two additional families (H33 and H27) with an inherited cerebellar ataxia
similar to that described in the AUS1 family.
Result: The affected H33 and H27 family members have a deletion at the SCA15 locus from
SUMF1 through IPTR1; the unaffected family members do not. (premise, Argument 9)
Conclusion (implicit): A deletion in ITPR1-SUMF1 may be the cause of the ataxia disorder in the
affected H33 and H27 family members. (conclusion, Argument 9 – Method of Difference)
Report Conclusion:
In three families [AUS1, H33, H27] cerebellar ataxia segregated with a deletion in SUMF1-ITPR1, not
observed in controls. (premise, Argument 10)
The deletion in ITPR1-SUMF1 is the cause of SCA15 in those families. (conclusion, Argument 10 –
Method of Difference; premise, Argument 12)
¶8
Report Previous Research:
Homozygous mutation of SUMF1 results in autosomal recessive multiple sulfatase deficiency…
No co-occurrence of ataxia has been described in heterozygous parents [i.e. who have one copy of a
SUMF1 mutation] of patients with multiple sulfatase deficiency (premise Argument 11).
It is improbable that the deletion of SUMF1 … itself causes or contributes to
SCA15 (conclusion Argument 11 – Failed Method of Agreement; premise Argument 12).
[Therefore, deletion in ITPR1 is the likely cause of ataxia in the three families (implicit conclusion,
Argument 12 – Eliminate Difference)]
Mutation of ITPR1 is biologically plausible as a cause of ataxia (conclusion, Argument 13 – Consistent
Explanation
18th Workshop on Computational Models of Natural Argument 13
Floris Bex, Floriana Grasso, Nancy Green (eds)
16th July 2017, London, UK