Synopsis

Overview of the topic-agnostic attributes compiled from the Ph.D. dissertation of Khalid Al-Khatib: Computational Analysis of Argumentation Strategies.

Attributes at ADU Level

Attribute Attribute Classes Data Genre & Source Size Topics Reference

ADU roles

1. Major claim

2. Claim

3. Premise

 

Persuasive Essays

402 essays, 7116 sentences. 6089 argument components, 751 major claims, 1506 claims, and 3832 premises

 

[17]

Verifiability

1. Verifiable-public

2. Verifiable-private

3. Non-verifiable

Online discussion.

eRulemaking platform: http://www.regulationroom.org/

9476 sentences and clauses from 1047 comments.

1. Airline Passenger Rights (serving peanuts on the plane, tarmac delay contingency plan, oversales of tickets, baggage fees and other airline traveller rights)

2. Home Mortgage Consumer Protection (loss mitigation, accounting error resolution, etc.)

[12]

Illocutions - Speech acts

1. A Report (Re)

2. A Report-Author (RA)

3. Idents (Id)

4. Evaluatives (Ev),

5. Estimates (Es)

6. Commitments (Co)

7. Directives (Di)

8. Present-Hypothetic-Situation (Hy)

German hotel reviews from a product review website

 

250 German hotel reviews

1100 connectives with 2350 related segments

 

[18]

 

Semantic types of claims

1. Interpretation

2. Evaluation

2.1 Evaluation-rational

2.2 Evaluation-emotional

3. Agreement or disagreement

ChangeMyView discussions

78 discussion threads comprise 278 turns of dialogue consisting of 2615 propositions in 2148 total sentences.

 

[7]

 

1. Fact

2. Value

3. Policy

 

Persuasive essays

 

102 essays randomly chosen from the Argument Annotated Essays corpus (Stab and Gurevych, 2014).

567 claims with on of the proposed types

 

[4]

Semantic types of premises

1. Logos

2. Pathos

3. Ethos

ChangeMyView discussions

78 discussion threads comprise 278 turns of dialogue consisting of 2615 propositions in 2148 total sentences.

 

[7]

 

1. Study

2. Factual

3. Opinion

4. Reasoning

Arguments from debate.org

200 debates, 450 claims, and 621 citation articles with about 53000 sentences. 995 sentences are identified as supporting arguments. Among those, 95 (9.55%) are labelled as study, 497 (49.95%) as factual, 363 (36.48%) as opinion, and 40 (4.02%) as reasoning.

 

[8]

 

1. Real example

2. Invented instance

3. Analogy

4. Testimony

5. Statistics

6. Definition

7. Common know.

8. Warrant

Persuasive essays

 

102 essays randomly chosen from the Argument Annotated Essays corpus (Stab and Gurevych, 2014).

707 premises with one of the types

 

[4]

ADU evidence types

1. Common Ground

2. Assumption

3. Testimony

4. Statistics

5. Anecdote

6. Other

News Editorials: aljazeera.com, foxnews.com, and theguardian.com

300 editorials, 14313 units

 

[1]

 

1. Anecdote

2. Testimony (expert)

3. Statistic (study)

 

547 Wikipedia articles

6353 evidence

58 different topics selected at random from Debatabase

[15]

ADU semantic types

1. Testimony

2. Fact

3. Value

4. Policy

5. Rhetorical Statement

ChangeMyView discussions

345 posts

 

[10]

 

1. Proposition of Non-Experiential Fact (fact)

2. Proposition of Experiential Fact (testimony)

3. Proposition of Value (value)

4. Proposition of Policy (policy)

5. Reference to a Resource (reference)

eRulemaking platform: http://www.regulationroom.org/

 

4931 elementary unit and 1221 support relation annotations

Consumer Debt Collection Practices (CDCP) rule by the Consumer Financial Protection Bureau (CFPB)

[13]

Attributes at Argument Level

Attribute Attribute Classes Data Genre & Source Size Topics Reference

Persuasive Acts

1. Reason

2. Deontic Appeal

3. Popularity

4. Redefinition

5. Empathy

6. Outcome

7. Impt Person

8. Favors/Debts

9. Consistency

10. Good/Bad Traits

11. Scarcity

Blogs that were gathered from Blogger.com

 

30 posts,

Passage level annotation

1162 passages

 

[2]

 

Argument types

1. Monologs

2. Factual

3. Emotional

 

Arguments from ProCon.org

5,185 arguments

death penalty, gay marriage, climate change, abortion, evolution and gun control

[9]

Argument scheme

1. Argument from example

2. Argument from cause to effect

3. Practical reasoning

4. Argument from consequences

5. Argument from verbal classification

Araucaria dataset: 660 manually annotated arguments from various sources, such as newspapers and court cases

393 arguments belong to one of the five schemes

 

[6]

 

1. I:Causal

2. I:Mereorogical

3. I:Definitional

4. E:Practical

5. E:Alternatives

6. E:Opposition

7. E:Analogy

8. C:Authority

Persuasive essays

60 (33.5%) Intrinsic:Causal,
46 (25.7%) Intrinsic:Mereological,
16 (8.9%) Intrinsic: Definitional,
28 (15.6%) Extrinsic:Practical Evaluation,
3 (1%) Extrinsic:Alternatives,
3 (1%) Extrinsic:Opposition, and
23 (12.8%) NoArgument.

 

[11]

 

 

1. Argument from Consequences

arg-microtexts corpus (Peldszus and Stede, 2015)

89 texts

 

[14]

 

1. Argument from Example

2. Argument from Cause to Effect

3. Argument from Effect to Cause

4. Practical Reasoning

5. Argument from Inconsistency

 

Penn Discourse Treebank (PDTB)

216 Arguments

 

[3]

 

 

Attributes at Discourse Level

Attribute Attribute Classes Data Genre & Source Size Topics Reference

Persuasion effect

1. Challenging

2. Reinforcing

3. No effect

News Editorials

1000 articles from New York Times

 

[5]

Discourse Mode

1. Narration

2. Exposition

3. Description

4. Argument

5. Emotion expressing

Persuasive Essays

415 essays

 

[16]

References

[1] Khalid Al-Khatib, Henning Wachsmuth, Johannes Kiesel, Matthias Hagen, and Benno Stein. A News Editorial Corpus for Mining Argumentation Strategies. In Yuji Matsumoto and Rashmi Prasad, editors, 26th International Conference on Computational Linguistics (COLING 2016), pages 3433-3443. Association for Computational Linguistics, December 2016.
[2] Pranav Anand, Joseph King, Jordan Boyd-Graber, Earl Wagner, Craig Martell, Doug Oard, and Philip Resnik. Believe me—we can do this! annotating persuasive acts in blog text, 2011.
[3] Elena Cabrio, Sara Tonelli, and Serena Villata. A natural language account for argumentation schemes. In Matteo Baldoni, Cristina Baroglio, Guido Boella, and Roberto Micalizio, editors, AI*IA 2013: Advances in Artificial Intelligence, pages 181-192, Cham, 2013. Springer International Publishing.
[4] Winston Carlile, Nishant Gurrapadi, Zixuan Ke, and Vincent Ng. Give me more feedback: Annotating argument persuasiveness and related attributes in student essays. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 621-631, Melbourne, Australia, July 2018. Association for Computational Linguistics.
[5] Roxanne El Baff, Henning Wachsmuth, Khalid Al-Khatib, and Benno Stein. Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus. In Anna Korhonen and Ivan Titov, editors, 22nd Conference on Computational Natural Language Learning (CoNLL 2018), pages 454-464. Association for Computational Linguistics, October 2018.
[6] Vanessa Wei Feng and Graeme Hirst. Classifying arguments by scheme. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 987-996, Portland, Oregon, USA, June 2011. Association for Computational Linguistics.
[7] Christopher Hidey, Elena Musi, Alyssa Hwang, Smaranda Muresan, and Kathy McKeown. Analyzing the semantic types of claims and premises in an online persuasive forum. In Proceedings of the 4th Workshop on Argument Mining, pages 11-21, Copenhagen, Denmark, September 2017. Association for Computational Linguistics.
[8] Xinyu Hua and Lu Wang. Understanding and detecting supporting arguments of diverse types. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 203-208, Vancouver, Canada, July 2017. Association for Computational Linguistics.
[9] Stephanie Lukin, Pranav Anand, Marilyn Walker, and Steve Whittaker. Argument strength is in the eye of the beholder: Audience effects in persuasion. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 742-753, Valencia, Spain, April 2017. Association for Computational Linguistics.
[10] Gaku Morio, Ryo Egawa, and Katsuhide Fujita. Revealing and predicting online persuasion strategy with elementary units. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6274-6279, Hong Kong, China, November 2019. Association for Computational Linguistics.
[11] Elena Musi, Debanjan Ghosh, and Smaranda Muresan. Towards feasible guidelines for the annotation of argument schemes. In Proceedings of the Third Workshop on Argument Mining (ArgMining2016), pages 82-93, Berlin, Germany, August 2016. Association for Computational Linguistics.
[12] Joonsuk Park and Claire Cardie. Identifying appropriate support for propositions in online user comments. In Proceedings of the First Workshop on Argumentation Mining, pages 29-38, Baltimore, Maryland, June 2014. Association for Computational Linguistics.
[13] Joonsuk Park and Claire Cardie. A corpus of eRulemaking user comments for measuring evaluability of arguments. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, May 2018. European Language Resources Association (ELRA).
[14] Paul Reisert, Naoya Inoue, Tatsuki Kuribayashi, and Kentaro Inui. Feasible annotation scheme for capturing policy argument reasoning using argument templates. In Proceedings of the 5th Workshop on Argument Mining, pages 79-89, Brussels, Belgium, November 2018. Association for Computational Linguistics.
[15] Ruty Rinott, Lena Dankin, Carlos Alzate Perez, Mitesh M. Khapra, Ehud Aharoni, and Noam Slonim. Show me your evidence - an automatic method for context dependent evidence detection. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 440-450, Lisbon, Portugal, September 2015. Association for Computational Linguistics.
[16] Wei Song, Dong Wang, Ruiji Fu, Lizhen Liu, Ting Liu, and Guoping Hu. Discourse mode identification in essays. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 112-122, Vancouver, Canada, July 2017. Association for Computational Linguistics.
[17] Christian Stab and Iryna Gurevych. Annotating argument components and relations in persuasive essays. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pages 1501-1510, Dublin, Ireland, August 2014. Dublin City University and Association for Computational Linguistics.
[18] Manfred Stede and Andreas Peldszus. The role of illocutionary status in the usage conditions of causal connectives and in coherence relations. Journal of Pragmatics, 44(2):214 - 229, 2012. Causal connectives in discourse: a cross-linguistic perspective.