Towards Relation-based Argumentation Mining? Francesca Toni - - PowerPoint PPT Presentation

Towards Relation-based Argumentation Mining?

Computational Logic and Argumentation

Francesca Toni

Department of Computing Imperial College London

Dagstuhl seminar on Natural Language Argumentation, April 2016

Outline

From

• Structured argumentation (ABA) and

Abstract Argumentation (AA) for rule-based arguments and beyond to

• Bipolar argumentation and Quantitative

Argumentation Debates (QuADs) supported by and supporting

• Mining of attack/support/neither relations

amongst Arguments

Structured Argumentation with Conflicting rules

YOU ARE COVERED FOR: UK and EU Breakdown Assistance for account holder(s) in any private car they are travelling in YOU ARE NOT COVERED FOR: private cars not registered to the account holder(s) unless the account holder(s) are in the vehicle at the time of the breakdown

• Default logic (Reiter 1980)

ABA with conflicting rules

contrary: contrary:

ABA/AA semantics with conflicting rules

A set of arguments is admissible if it is conflict- free and it attacks every argument that attacks it

Dung 1995; Bondarenko, Dung, Kowalski, Toni 1997

Mary(friend’s car, in car) covered as travelling in private car attacked by not covered as car not registered to Mary attacked by Mary in car at time of breakdown

ABA for decision making in medicine

Mocanu, Fan, Toni, Williams, Chen 2014-16

AA for Smart Electricity

Makriyiannis, Lung, Craven, Toni, Kelly 2014-16

AA for Reinforcement Learning

robocup Gao, Toni 2012-15

Outline

From

• Structured argumentation (ABA) and

Abstract Argumentation (AA) for rule-based arguments and beyond to

• Bipolar argumentation and Quantitative

Argumentation Debates (QuADs) supported by and supporting

• Mining of attack/support/neither relations

amongst Arguments

Baroni, Romano, Toni , Aurisicchio, Bertanza (2015 )

REUSE OF SLUDGE PRODUCED BY WASTEWATER TREATMENT PLANTS

exit stay support

Justified vs Weak/Strong Arguments

…. …. …. weaker stronger …. …. if opinions have equal a-priori strength exit stay

IBIS (Issue Based Information System, Kunz and Rittel 1970)

www.arganddec.com Aurisicchio, Baroni, Pellegrini, Toni (2015)

www.arganddec.com

Computing strength

• All opinions have a base score (a-priori

strength) in [0,1]

• Opinions (arguments) attack or

support other opinions (arguments)

• Debates are (sets of) trees

Computing strength

To compute the strenght SF of argument x the function C combines three elements:

the base score of x a factor summarizing the attackers of x a factor summarizing the supporters of x using the sequence of the strengths of the attackers of x

Romano, Rago, Baroni, Toni , Aurisicchio, Bertanza 2013-16

Combining all factors: properties

• The order of attackers/supporters does

not matter

• Adding a supporter will not lower strength

= s s'



Combining all factors: more properties

• The smaller the strength of an

attacker/supporter the smaller its impact s s' s0

if s0 is very small then s' is almost the same as s

Outline

From

• Structured argumentation (ABA) and

Abstract Argumentation (AA) for rule-based arguments and beyond to

• Bipolar argumentation and Quantitative

Argumentation Debates (QuADs) supported by and supporting

• Mining of attack/support/neither relations

amongst Arguments

Argument mining

Carstens, Toni 2015

Outline

From

• Structured argumentation (ABA) and

Abstract Argumentation (AA) for rule-based arguments and beyond to

• Bipolar argumentation and Quantitative

Argumentation Debates (QuADs) supported by and supporting

• Mining of attack/support/neither relations

amongst Arguments

Relation-based Argument Mining (RbAM)

Classifier (BOW) argument base Strength calculation

Argument base for RbAM

e.g.

• 1. The word “and” appears in Child

(supports Sup)

• 2. The word “and” is the first word in Child

(supports the keyword argument)

• 1. Keyword arguments (73)
• 2. First word arguments (10)
• 3. Sentiment arguments (3)
• 4. Similarity arguments (12)
• 5. Negation arguments (73)

Corpora for RbAM

• Internet Argument Corpus (IAC) from

4forums [Walker et al LREC 2012]

• 18 sub-corpora of AIFdb
• (new) News articles corpus:
• Comprised of 2,274 sentence pairs:
• 413 Attack, 456 Support, 1,385 Neither
• Fleiss’ kappa = 0.4287

RbAM: some experiments

Conclusions

• Several argumentation frameworks and

semantics (for evaluating arguments)

• Bipolar argumentation and QuAD for

natural language arguments

• Quantitative strength as a useful measure of

dialectical strength of mined arguments? Goodness of Argument Mining?

• Argumentation to help Argument Mining?