|
Manual Argument Analysis
|
| Nancy L Green. 2015b. Annotating evidence-based
argumentation in biomedical text. In 2015 IEEE
International Conference on Bioinformatics and
Biomedicine (BIBM), pages 922–929. IEEE.
|
Biomedical articles |
Analyzed evidence based arguments in four full-text articles on genetic variants that may cause human health problems and created a preliminary catalog of argumentation schemes |
|
|
| Nancy Green. 2017a. Manual identification of arguments with implicit conclusions using semantic rules
for argument mining. In Proceedings of the 4th
Workshop on Argument Mining, pages 73–78.
|
Biomedical articles |
Evaluate human analysts’ ability to identify the argumentation scheme and premises of an argument having an implicit conclusion |
|
|
| Nancy Green. 2018a. Proposed method for annotation
of scientific arguments in terms of semantic relations
and argument schemes. In Proceedings of the 5th
Workshop on Argument Mining, pages 105–110.
|
Biomedical Genetics articles |
Provide a method for semantic rep- resentation of arguments that can be used in empirical studies of scientific discourse as well as to support appli- cations such as argument mining |
|
|
| Heather Graves, Roger Graves, Robert E Mercer, and
Mahzereen Akter. 2014. Titles that announce argumentative claims in biomedical research articles. In
Proceedings of the First Workshop on Argumentation Mining, pages 98–99.
|
Biomedical articles |
Analyses article title as a potential source of claims and finds that fre- quency of verbs in titles of experimen- tal research articles has increased over time |
|
|
| Heather Graves, Roger Graves, Robert E Mercer, and
Mahzereen Akter. 2014. Titles that announce argumentative claims in biomedical research articles. In
Proceedings of the First Workshop on Argumentation Mining, pages 98–99.
|
Biomedical articles |
Analyses article title as a potential source of claims and finds that fre- quency of verbs in titles of experimen- tal research articles has increased over time |
|
|
|
Corpus Creation and New Annotation Schemes
|
| Nancy Green. 2014. Towards creation of a corpus for
argumentation mining the biomedical genetics research literature. In Proceedings of the first workshop on argumentation mining, pages 11–18.
|
Biomedical Genetics articles |
Argument annotation scheme: Premise (Data, Warrant) and Conclu- sion |
|
Theoretical challenges to cre- ate an argument corpora |
| Nancy Green. 2015a. Identifying argumentation
schemes in genetics research articles. In Proceedings of the 2nd Workshop on Argumentation Mining,
pages 12–21.
|
Biomedical Genetics articles |
Identification of argumentation schemes with specification of ten semantically distinct argumentation schemes |
|
Annotation guidelines for ar- gumentation corpora |
| Simone Teufel and Marc Moens. 1999. Discourselevel argumentation in scientific articles: human and
automatic annotation. In Towards Standards and
Tools for Discourse Tagging.
|
Chemistry, Com- putational Lin- guistics |
Detect argument zones in scientific ar- ticles |
Proposed a scheme and annotated 15 argument zone categories for 39 papers (5,374 sentences) |
|
| Christian Kirschner, Judith Eckle-Kohler, and Iryna
Gurevych. 2015. Linking the thoughts: Analysis
of argumentation structures in scientific publications.
In Proceedings of the 2nd Workshop on Argumentation Mining, pages 1–11.
|
Scientific articles (Educational and Developmental Psychology) |
New annotation scheme to identify argumentative relations - support, at- tack, detail, sequence |
|
Study of the annotation strat- egy across 24 articles, an anno- tation tool, a new graph-based inter-annotation measure |
| Anne Lauscher, Goran Glavaš, and Simone Paolo
Ponzetto. 2018b. An argument-annotated corpus of
scientific publications. In Proceedings of the 5th
Workshop on Argument Mining, pages 40–46.
|
Computer Graph- ics scientific pub- lications |
Proposed a new argument-annotated dataset of scientific publications |
Adapted Toulmin’s model for argumentative components: Back- ground Claim, Own Claim, Data. Relation between argumentative components: support, contradicts, same claim |
Investigation on link between argumentative nature of scien- tific publications and rhetori- cal aspects such as discourse categories or citation contexts. |
| Mohammed Alliheedi, Robert E Mercer, and Robin Cohen. 2019. Annotation of rhetorical moves in biochemistry articles. In Proceedings of the 6th Workshop on Argument Mining, pages 113–123. |
Biochemistry arti- cles |
Determine rhetorical moves in the ar- gument structure of biomedical arti- cles |
Annotated method sections of 105 text files based on a new annotation scheme for identifying the struc- tured representation of knowledge in a set of sentences describing the experimental procedures |
|
| Yufan Guo, Ilona Silins, Roi Reichart, and Anna Korhonen. 2012. Crab reader: A tool for analysis and
visualization of argumentative zones in scientific literature. In Proceedings of COLING 2012: Demonstration Papers, pages 183–190.
|
Biomedical papers |
Introduce a tool for analysis and visu- alizing argument structure (based on AZ), and also facilitate expert AZ an- notation |
Used HTML, JavaScript, PHP, XML for the annotation tool; SVM classifier using features from Guo et al. (2011) |
Interactive annotation via ac- tive learning; CRAB Reader allows user to define AZ schemes; AZ can be per- formed on each word, sen- tence, paragraph, document level |
| An Yang and Sujian Li. 2018. SciDTB: Discourse dependency TreeBank for scientific abstracts. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short
Papers), pages 444–449, Melbourne, Australia. Association for Computational Linguistics.
|
Scientific ab- stracts from ACL Anthology |
Construct a domain-specific dis- course treebank annotated on scientific articles |
798 segmented abstracts were la- belled by 5 annotators in 6 months. 506 abstracts were annotated more than twice separately by different annotators. In total, SciDTB con- tains 798 unique abstracts with 63% labelled more than once and 18,978 discourse relations. |
|
|
Automatic Argument Unit Identification
|
| Nancy L Green. 2017b. Argumentation mining in scientific discourse. In CMNA@ ICAIL, pages 7–13. |
Biomedical, Biological articles |
Argumentation extraction |
Semantic rule-based approach |
Demonstrates the need for
a richer model of interargument relationships in
biomedical/biological research articles.
|
| Anne Lauscher, Goran Glavaš, and Kai Eckert. 2018a.
Arguminsci: A tool for analyzing argumentation and
rhetorical aspects in scientific writing. Association
for Computational Linguistics.
|
Computer Graphics
scientific publications
|
A toolkit for rhetorical analysis of
argument component identification,
discourse role classification, subjective aspect classification, citation
context classification, summary relevance classification
|
Token-level sequence labelling, sentence-level classification using Bi-lSTM |
Command-line tool, RESTful API,
web application
|
| Anne Lauscher, Goran Glavaš, Simone Paolo Ponzetto,
and Kai Eckert. 2018c. Investigating the role of argumentation in the rhetorical analysis of scientific
publications with neural multi-task learning models.
In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages
3326–3338.
|
Computer Graphics
scientific publications
|
Proposed two neural multi-task
learning (MTL) models for argumentative analysis based on the
tasks in (Lauscher et al., 2018a)
|
Bi-LSTM based simple MTL
model for sentence-level classification, hierarchical MTL
for sequence labelling
|
Adapted Toulmin’s model for
argumentative components: Background Claim, Own Claim, Data.
Relation between argumentative
components: support, contradicts,
same claim
|
| Simone Teufel. 2014. Scientific argumentation detection as limited-domain intention recognition. In
ArgNLP.
|
Chemistry, Computational Linguistics,
Agriculture
|
Views scientific argumentation detection as limited-domain intent
recognition
|
Model based on recognition of
28 rhetorical moves in tex
|
|
| Yufan Guo, Anna Korhonen, and Thierry Poibeau.
2011. A weakly-supervised approach to argumentative zoning of scientific documents. In Empirical
Methods in Natural language Processing (EMNLP).
|
Biomedical abstracts |
Investigating a weakly-supervised
approach for AZ detection when a
limited amount of training data is
available
|
Features like location, word
bi-gram, verb, verb cues,
PoS, grammatical relations,
subj/obj, voice are used with
ASVM, ASSVM, TSVM,
SSCRF
|
Conclusion that location of AZs are
super important, directions to facilitate easy porting of AZ schemes to
new NLP tasks and domains
|
| Xiangci Li, Gully Burns, and Nanyun Peng. 2019.
Scientific discourse tagging for evidence extraction.
arXiv e-prints, pages arXiv–1909.
|
Biomedical publications |
Automatic evidence extraction using scientific discourse tagging
based on classification by de Waard
et al. (2009)
|
sentence-level sequential labelling using BiLSTM-CRF +
Attention
|
Leveraging scientific discourse tagging for evidence fragment detection |
| Titipat Achakulvisut, Chandra Bhagavatula, Daniel
Acuna, and Konrad Kording. 2019. Claim extraction in biomedical publications using deep discourse model and transfer learning. arXiv preprint
arXiv:1907.00962.
|
Biomedical abstracts |
Automated claim extraction |
Neural discourse tagging
model based on a pre-trained
BilSTM+CRF followed by
transfer learning and fine
tuning on a expert annotated
dataset
|
New dataset of 1,500 expertannotated biomedical abstracts
indicating whether the sentence
presents a scientific claim.
|
| Hospice Houngbo and Robert E Mercer. 2014. An
automated method to build a corpus of rhetoricallyclassified sentences in biomedical texts. In Proceedings of the first workshop on argumentation mining,
pages 19–23.
|
Biomedical articles |
Identify the components of IMRaD
rhetorical structure in biomedical
papers
|
Applied a few heuristics to
construct a corpus and used
machine learning techniques
(Naive Bayes and SVM)
to classify sentences into
Method,Result or Conclusion
|
|
| José María González Pinto, Serkan Celik, and WolfTilo Balke. 2019. Learning to rank claim-evidence
pairs to assist scientific-based argumentation. In International Conference on Theory and Practice of
Digital Libraries, pages 41–55. Springer.
|
Biomedical papers |
Claim-evidence matching as a
learning to rank problem where
goal is to find evidence in the
form of a paper to make a natural
language claim appear credible; to
assist scientific argumentation
|
Rhetoric Classification Task
and Claim-Evidence Rank
Task using NB-BoW, SVMBoW, CNN on data from
a Wikipedia dump with
word2vec trained on PubMed
Central UMLS, SemMedDB
databases
|
Augmenting "prestige" meta-data
features for a paper improved performance, to rank claim-evidence
pairs, a model should account for
other semantic properties beyond
simple content-matching
|
| Syeed Ibn Faiz and Robert E Mercer. 2014. Extracting
higher order relations from biomedical text. In Proceedings of the First Workshop on Argumentation
Mining, pages 100–101.
|
Biomedical papers |
Extraction of connections or
“higher order relations" between
biomedical relations (relationship
between biomedical entities). The
higher order relation conveys a
causal sense, which indicates that
the latter relation causes the earlier
one.
|
In the first stage, the authors use a discourse relation
parser to extract the explicit
discourse relations from text.
In the second stage, the authors analyze each extracted
explicit discourse relation to
determine whether it can produce a higher order relation
|
Pilot evaluation on AIMed corpus
for protein-protein interaction prediction: identify the full argument
extent which contain the biomedical entities
|
| Antonio Jimeno Yepes, James G Mork, and Alan R
Aronson. 2013. Using the argumentative structure
of scientific literature to improve information access.
In Proceedings of the 2013 Workshop on Biomedical
Natural Language Processing, pages 102–110.
|
MEDLINE/PubMed
abstracts
|
An evaluation of several learning
algorithms to label abstract text
with argumentative labels, based
on structured abstracts available in
MEDLINE/PubMed
|
Naive Bayes, SVM, Logistic Legression, CRF,
AdaBoostM1 as classifiers for
the argumentation labels on
abstract text. In addition to
textual features, the position
of the sentence or paragraph
from the beginning of the
abstract is used
|
A data set to compare and evaluate GeneRIF indexing approaches.
The sentence annotation are: Expression, Function, Isolation, NonGeneRIF, Other, Reference, and
Structure on MEDLINE articles.
|
|
Automatic Argument Structure Identification
|
| Christian Stab, Christian Kirschner, Judith EckleKohler, and Iryna Gurevych. 2014. Argumentation
mining in persuasive essays and scientific articles
from the discourse structure perspective. In Proceedings of the Workshop on Frontiers and Connections between Argumentation Theory and Natural
Language Processing, Forlì-Cesena, Italy, July 21-
25, 2014, volume 1341 of CEUR Workshop Proceedings. CEUR-WS.org.
|
Scientific articles I |
Identification of argumentation
structures
|
Argument unit identification
and relation extraction
|
An evaluation dataset of 20 scientific full-texts annotated with argument relations ‘support’, ‘attack’,
‘sequence’
|
| Valéria D Feltrim, Simone Teufel, Maria Graças V das
Nunes, and Sandra M Aluísio. 2006. Argumentative zoning applied to critiquing novices’ scientific
abstracts. In Computing Attitude and Affect in Text:
Theory and Applications, pages 233–246. Springer.
|
Brazilian PhD Theses |
A system to detect argumentative
structures in text
|
The annotation scheme has
the following rhetorical categories: Background, Gap, Purpose, Methodology, Results,
Conclusion and Outline. A
Naive Bayes classifier to identify the argumentative units
|
Porting of Argumentative Zoning
(AZ) from English to Portuguese. A
pilot system to demonstrate the effectiveness of AZ for a critiquing
tool to support academic writing
|
| Pablo Accuosto and Horacio Saggion. 2020. Mining arguments in scientific abstracts with discourselevel embeddings. Data & Knowledge Engineering,
129:101840.
|
Computational
linguistics abstracts
|
Argument unit identification and relation extraction |
Explore two transfer learning
approaches in which discourse
parsing is used as an auxiliary
task when training argument
mining models
|
Propose a new annotation schema
and use it to augment a corpus of
computational linguistics abstracts
that had previously been annotated
with discourse units and relations
|
| Ningyuan Song, Hanghang Cheng, Huimin Zhou, and
Xiaoguang Wang. 2019. Argument structure mining
in scientific articles: a comparative analysis. In 2019
ACM/IEEE Joint Conference on Digital Libraries
(JCDL), pages 339–340. IEEE.
|
Information Science
and Biomedical articles
|
Apply sequential pattern mining
to analyse the common argument
structure in two scientific domains
(Information science and biomedical science)
|
|
|
|
Applications
|
| Pablo Accuosto and Horacio Saggion. 2019. Transferring knowledge from discourse to arguments: A
case study with scientific abstracts. In Stein B,
Wachsmuth H, editors. Proceedings of the 6th Workshop on Argument Mining; 2019 Aug 1; Florence,
Italy. Stroudsburg: Association for Computational
Linguistics; 2019. p. 41-51. ACL (Association for
Computational Linguistics).
|
Computational
Linguistics abstracts I
|
Leverage existing discourse parsing
RST annotations (Stede et al., 2017)
to identify argumentative components
and relations
|
Transfer learning to improve the
performance of argument mining
tasks trained with a small corpus of
60 abstracts by leveraging the discourse annotations available in the
full SciDTB () corpus; sequence labelling task with dependency-based
word embeddings, contextualized
ElMo, RST encodings, GloVe
|
Enrich a subset of SciDTB
with additional layer of argumentation, EDUs as minimal span for annotation, pilot task to predict acceptance/rejection using automatically identified argumentative
components and relations
|
| Danish Contractor, Yufan Guo, and Anna Korhonen.
2012. Using argumentative zones for extractive summarization of scientific articles. In Proceedings of
COLING 2012, pages 663–678.
|
Biomedical
papers
|
Leveraging on AZ features for extractive summarization of scientific articles |
Used AZ categories as features in final sentence selection process + additionally used verbs, tf-idf, citation
and reference occurrences, locative
features for classification to generate initial set of candidate sentences.
Then performed k-Means cluatering to group similar sentences and
select the centroid from each group
to generate the summary (redundancy elimination)
|
Demonstrated the efficacy of
weakly-supervised AZ classifier for less training data by
Guo et al. (2011) for scientific
article summary extraction
|
| Simone Teufel and Marc Moens. 2002. Summarizing scientific articles: experiments with relevance
and rhetorical status. Computational linguistics,
28(4):409–445.
|
Computational
Linguistics papers
|
Summarize scientific articles by concentrating on the rhetorical status of
statements in an article
|
Developed an algorithm to select
content from articles and classify them into rhetorical categories which integrate argumentation structure in scientific papers
|
|
| Valéria D Feltrim and Simone Teufel. 2004. Automatic
critiquing of novices’ scientific writing using argumentative zoning. In Proc. AAAI spring symposium
exploring affect and attitude in text.
|
Brazilian PhD
Theses in Computer Science
|
Integrated Argumentative Zoning into
an automatic Critiquing Tool for Scientific Writing in Portuguese (SciPo)
|
Implemented a set of 7 features, derived from the 16 used by (Teufel
and Moens, 2002), Naive Bayes as
the classifier
|
Port the feature detection stage
of AZ from English to Portuguese, a human annotation
experiment to verify the reproducibility of the annotation
scheme, intrinsic evaluation of
AZ-part of SciPo
|
| Anita de Waard, S Buckingham Shum, Annamaria
Carusi, Jack Park, Matthias Samwald, and Ágnes
Sándor. 2009. Hypotheses, evidence and relationships: The hyper approach for representing scientific
knowledge claims.
|
IProduction and
Manufacturing,
Biomedical,
Law/Legal
|
The authors present SALT (Semantically Annotated LATEX), a semantic
authoring framework that enables the
externalization of the argumentation
and rhetoric captured in scientific publication’s content.
|
The annotation framework is a layered organization of three ontologies: the Document Ontology - capturing the linear structure of the
publication, the Rhetorical Ontology - modeling the rhetorical and
argumentation, and the Annotation
Ontology - linking the rhetoric and
argumentation to the publication’s
structure and content.
|
A LATEX and MS-Word plugin for semantic annotation of
scientific publications as per
SALT scheme
|
| Tudor Groza, Siegfried Handschuh, and Stefan Decker.
2011. Capturing rhetoric and argumentation aspects
within scientific publications. In Journal on data semantics XV, pages 1–36. Springer.
|
|
Proposal to extract knowledge from
articles to allow the construction of
a system where a specific scientific
claim is connected, through trails of
meaningful relationships, to experimental evidence. To improve access to collections of scientific papers
represented as networks of collection
of claims that have a defined epistemic value, with links to experimental evidence and argumentative relationships to other statements and evidence. The authors coin this conceptual approach ‘Hypotheses, Evidence
and Relationships’ (HypER).
|
|
|
| Bei Yu, Jun Wang, Lu Guo, and Yingya Li. 2020.
Measuring correlation-to-causation exaggeration in
press releases. In Proceedings of the 28th International Conference on Computational Linguistics,
pages 4860–4872, Barcelona, Spain (Online). International Committee on Computational Linguistics.
|
PubMed papers
and news articles
|
Study exaggeration in press releases |
Developed a new corpus and trained
models that can identify causal
claims in the main statements in
a press release. By comparing
the claims made in a press release with the corresponding claims
in the original research paper, the
authors found that 22% of press
releases made exaggerated causal
claims from correlational findings
in observational studies.
|
|
| Xiangci Li, Gully Burns, and Nanyun Peng. 2021. Scientific discourse tagging for evidence extraction. In
Proceedings of the 16th Conference of the European
Chapter of the Association for Computational Linguistics: Main Volume, pages 2550–2562, Online.
Association for Computational Linguistics.
|
Biomedical
papers
|
demonstrate the benefit of leveraging scientific discourse tags for downstream tasks such as claim-extraction
and evidence fragment detection
|
Develop a sentence-level sequence
tagging model to label discourse
types for each sentence in a paragraph
|
|