IDENTIFYING TYPES OF ARGUMENTS IN TEXT:

FIRST STEPS TOWARDS AN IDENTIFICATION PROCEDURE

November 10, 2010

Douglas Walton Distinguished Research Fellow CRRAR

Helping Informal Logic Students

• It would be helpful to have a more systematic procedure that can be used to help students of informal logic identify arguments of the kind they encounter in natural language texts.

• Examples include argumentative texts found in newspapers, magazines or on the Internet, that students want to analyze using standard methods of informal logic.

Douglas Walton Distinguished Research Fellow CRRAR

Example: Argument from Expert Op.

• Argument from expert opinion is made up of two distinctive premises and conclusion.

• Basically it says, so-and-so is an expert, so-and-so says that some proposition is true, therefore (defeasibly) this proposition is true.

• Identifying this particular type of argument would seem to be simple. For example, the method could use keywords, like the word ‘expert’.

• However, from experiences with teaching informal logic methods to students, there is a problem that occurs with some students who will immediately go to the Internet when asked to find examples of this kind of argument, and pick the first text they find containing the word ‘expert’.

Douglas Walton Distinguished Research Fellow CRRAR

Example: Ad Hominem Argument

• For something to be a genuine ad hominem argument, four requirements have to be met.

• There have to be two arguers who are engaging in some sort of argumentation with each other.

• One of the arguers has to have put forward an argument.

• The other arguer has to be attacking the first party’s argument.

• The other arguer has to be using personal attack for this purpose.

Douglas Walton Distinguished Research Fellow CRRAR

A Connected Task

• An automated argumentation tool for argument mining tool could go onto the Internet and collect arguments of specifically designated types, like argument from expert opinion.

• These two tasks are connected, because the most powerful method would be have human users apply the automated tool to identify arguments on a tentative basis in text, and then correct the errors made by the automated tool.

Douglas Walton Distinguished Research Fellow CRRAR

Comparable Research

• This kind of project has already been carried out by researchers in computational linguistics and law.

• They used argumentation schemes to identify types of arguments used in Supreme Court judgments in the International Court.

• They built a computational tool that could be used to identify types of arguments found in this corpus, and then hired coders to try to apply the tool to the corpus.

• This task was made easier by the fact that the court documents that provided their corpus was already classified using subheadings into different parts of the text that had different functions.

• So, for example, there is one section of the text where the arguments of the judges are presented.

Douglas Walton Distinguished Research Fellow CRRAR

Wikipedia Text mining, sometimes alternately referred to as text data mining,

roughly equivalent to text analytics, refers to the process of deriving high-quality information from text. High-quality information is typically derived through the divining of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling (i.e., learning relations between named entities).

Douglas Walton Distinguished Research Fellow CRRAR

Current Research on Argument Mining

• Ongoing research on this task shows great promise as a research direction in AI and law (Mochales-Palau and Moens 2007; Mochales and Leven, 2009; Mochales Palau and Moens, 2009, 100), and could be could be useful for many other purposes of text mining as well.

• Law is a good place to start, as the arguments in some legal bases are much better structured than the kinds of arguments one typically finds in everyday conversational argumentation.

Douglas Walton Distinguished Research Fellow CRRAR

MOCHALES PALAU, RaquelDepartment of Computer Science, Katholieke Universiteit LeuvenCelestijnenlaan 200A B-3001 Heverlee, BelgiumE-mail: raquel.mochales@cs.kuleuven.beNationality: SpanishSoftware Engineer2006-2010: PhD Candidate in Informatics (Computer Science)Katholieke Universiteit Leuven (KUL), LeuvenDissertation title: Automatic detection and classification of arguments in a legal case

Douglas Walton Distinguished Research Fellow CRRAR

References to this Work• Marie-FrancineMoens, Raquel Mochales Palau, Erik Boiy and Chris Reed, Automatic Detection of Arguments

in Legal Texts, Proceedings of the International Conference on AI and Law (ICAIL 2007), Stanford, California, 2007, 225-230.

• Raquel Mochales-Palau and Marie-Francine Moens, Study on Sentence Relations in the Automatic Detection

of Argumentation in Legal Cases, Legal Knowledge and Information Systems: JURIX 2007, The Twentieth International Conference, ed. Arno R. Lodder and Laurens Mommers, Amsterdam IOS Press, 2007, 89-98.

• Raquel Mochales and Marie-Francine Moens, Study on the Structure of Argumentation in Case Law, Legal

Knowledge and Information Systems: JURIX 2008, ed. Enrico Francesconi, Giovanni Sartor and Daniela Tiscornia, Amsterdam IOS Press, 2008, 11-20, 89-98.

• Raquel Mochales and Aagje Leven, Creating an Argument Corpus: Do Theories Apply to Real Arguments?,

Proceedings of the 12th International Conference on Artificial Intelligence and Law, New York, Association for Computing Machinery, Inc., 2009, 21- 30.

• Raquel Mochales Palau and Marie-Francine Moens, Argumentation Mining: The Detection, Classification and

Structure of Arguments in Text, Proceedings of the 12th International Conference on Artificial Intelligence and Law, New York, ACM, 2009, 98-107.

Douglas Walton Distinguished Research Fellow CRRAR

1. Argument from Analogy2. Argument from a Verbal Classification3. Argument from Rule5. Argument for Exception to a Rule 6. Argument from Precedent 7. Practical Reasoning8. Lack of Knowledge Arguments9. Arguments from Consequences10. Fear and Danger Appeals11. Arguments from Alternatives and Opposites12. Pleas for Help and Excuses13. Composition and Division Arguments14. Slippery Slope Arguments15. Arguments from Popular Opinion16. Argument from Commitment17. Arguments from Inconsistency18. Ethotic Ad Hominem 19. Circumstantial Ad Hominem20. Argument from Bias21. Argument from Cause to Effect22. Argument from Correlation to Cause23. Argument from Evidence to a Hypothesis24. Abductive Reasoning25. Argument from Position to Know26. Argument from Expert Opinion27. Argument from Waste

Basic Argumentation Schemes

Argumentation Schemes Found in the European Court of Human Rights

ARGUMENTATION SCHEME %OCC % INCArgument from Sign 7.9 7.2Argument from Example 5.2 2.4Argument from Verbal Classification 4.7 5.1Argument from Commitment 0.6 0Circumstantial Argument Against the Person 0 0Argument from Position to Know 2.1 2.3Argument from Expert Opinion 2.7 0Argument from Evidence to a Hypothesis 0.3 0Argument from Falsification of a Hypothesis 4.1 0Argument from Correlation to Cause 1.1 1.7Argument from Cause to Effect 4.7 10.6Argument from Consequences 8.2 5.4Argument from Analogy 21.6 25.4Argument from Waste 2.3 0Argument from Popularity 1.2 0Argument from Popular Practice 2.4 2.2Ethotic Argument 0 0Negative Ethotic Argument 0.3 0Argument from Bias 0 0Argument from an Established Rule 19.6 28.0Argument from an Exceptional Case 0.4 1.7Argument from Precedent 6.1 1.7Argument from Gradualism 4.4 6.3Argument from Vagueness of a Verbal Classification 0 0Argument from Arbitrariness of a Verbal Classification 0 0Argument from Ignorance 0 0

Note that practical reasoning was not one of the schemes used in the study.

Douglas Walton Distinguished Research Fellow CRRAR

Transfer to Informal Logic

• It is encouraging that there are already some systems applying 27 argumentation schemes to legal texts that have been implemented and the results of this experimental work are very interesting so far (Mochales and Leven, 2009, 27).

• It is interesting to note that there were no identifications of instances of argument from ignorance in the corpus, and very few instances of argument from commitment were identified.

Douglas Walton Distinguished Research Fellow CRRAR

Two Distinct Tasks

• A prior task is that of identifying arguments as entities that are distinct from other kinds of entities that occur in natural language discourse, like explanations.

• The main task is that of identifying specific types of arguments.

Douglas Walton Distinguished Research Fellow CRRAR

Teaching Informal Logic Students• This kind of work represents a more systematic

continuation of the kind of thing that is being done in teaching courses on argumentation or informal logic.

• Over many years of teaching courses of this type, I always used basically the same method of starting to teach the students through the use of examples.

• I searched through magazines, newspapers, and the Internet, or whatever other sources of material were available, to find interesting examples of arguments from expert opinion, ad hominem arguments, and so forth.

Douglas Walton Distinguished Research Fellow CRRAR

My Experience of Teaching • From building up stocks of these cases and discussing and analyzing them

with my classes, I started to build up accounts of each of the types of arguments, the kinds of premises they have, the different varieties of them and so forth. However, I didn't do this collecting in any systematic way.

• The examples I found initially came from the Newsweek magazine I read at breakfast, or from the sections on informal logic in the many logic textbooks that use such examples.

• Eventually the experience that came from studying these examples led to the formulation of argumentation schemes, simple models used to represent the basic structure of each type of argument.

• The schemes turned out to be helpful as I continued to teach courses on argumentation, because they gave the students some guideposts to use in their attempts to identify, analyze and evaluate arguments (and sets of critical questions).

Douglas Walton Distinguished Research Fellow CRRAR

Usefulness of this Research• So it is not hard to see how a semi-automated procedure would

be extremely helpful for teaching courses in informal logic.• It would also have a much wider use, however. • It would be an extremely powerful tool for researchers in

informal logic. • They could collect masses of interesting data on particular types

of arguments that have long been studied in a more anecdotal way, and make the findings of the field of argumentation study much more powerful because it is would then be based on real documented data of a highly comprehensive sort.

Douglas Walton Distinguished Research Fellow CRRAR

There is also some other research that could be drawn on

Douglas Walton Distinguished Research Fellow CRRAR

Research Group of Iyad Rahwan

• In (Rahwan et al., 2011) the basic 27 Walton schemes are applied by classifying their components as ordinary premises, assumptions, exceptions and conclusions. Representing and Classifying Arguments on the Semantic Web, The Knowledge Engineering Review, to appear, 2011 .

• Statements may be classified as declarative or imperative. For example, in the scheme for argument from position to know the class of statement PositiontoHaveKnowledgeStmnt is defined as a species of declarative statement.

• Using these categories, it is possible to fully describe a scheme, like the scheme for argument from position to know, by stating the necessary and sufficient conditions for an instance to be classified as falling under this type.

• The method of identifying schemes is implemented in a web-based system called Avicenna (2009, 11-13). A user can search arguments on the basis of keywords and structural features (Rahwan et al., 2008, 12).

I'm a professor of applied computational linguistics at Potsdam University. My research and teaching activities mostly revolve around issues in discourse structure, automatic discourse understanding and generation. Contact Manfred StedeEmail: stede AT uni-potsdam.de

Discourse structure: A prerequisite for any intelligent text understanding is an account of the various aspects of discourse structure. My research had drawn inspiration from Rhetorical Structure Theory (Bill Mann, Sandra Thompson), and I looked into a variety of individual coherence relations such as Concession [lit], Contrast [lit] or (non-standard!) Substitution [lit]. In recent years, I have become sceptical on the structural theses of RST, though - see [lit] for my criticism on the notion of 'nuclearity', and [lit] for a proposal on a less constrained scheme of multi-level discourse annotation. A distinct level of discourse structure that interests me is argumentation [lit] [lit] in texts that convey opinion and attitude. Corpora, annotation, linguistic databases: As an empirical basis for discourse analyses, we are building up the Potsdam Commentary Corpus [lit]: German newspaper commentaries annotated on a variety of levels, including, for example, discourse connectives [lit].

Dr. Adam Zachary Wyner

Researcher in Computer Science and Linguistics

Academic AffiliationDepartment of Computer Science University College London, Gower Street, London, WC1E 6BT, United KingdomEmail: adam@wyner.info My research interests are interdisciplinary.My current research focuses on natural language engineering of argumentation, which is about designing and developing materials and software tools to support users in formulating and evaluating arguments which are expressed in natural language. Basically, users could conduct virtual debates in natural language arguments about a discussion topic such as waste management (or other policy or legal issues). The system would parse the input sentences, provide semantic translations, use ontologies, calculate the semantic relationships between statements already in the debate, and evaluate results. To carry out the project requires development of a range of resources and technologies: a corpus of arguments, application of computational linguistic text-mining and machine learning techniques, ontologies, controlled languages with a predictive editor, determination of argument relationships, and argumentation frameworks.

Argument Mining Using Highly Structured Argument RepertoireSafia Abbas Graduate School of Science and Technology, Niigata University8050, 2-cho, Ikarashi, Niigata, 950-2181 JAPAN safia@cs.ie.niigata-u.ac.jpHajime Sawamura Institute of Natural Science and Technology, Ikarashi, Niigata, 950-2181 JAPAN sawamura@ie.niigata-u.ac.jp

Abstract. Argumentation theory is considered an interdisciplinary research area. Its techniques and results have found a wide range of applications in both theoretical and practical branches of artificial intelligence, education, and computer science. Most of the work done in argumentation use the on-line textual data (i.e. unstructured or semi-structured) which is intractable to be processed. This paper reports a novel approach to build a Relational Argument DataBase (RADB) with managing tools for argument mining, the design of the RADB depends on the Argumentation Interchange Format Ontology(AIF) using "Walton Theory".The proposed structure aims to: (i) summon and provide a myriad of arguments at the user's fingertips, (ii) retrieve the most relevant results to the subject of search, (iii) support fast interaction between the different mining techniques and the existing arguments, and (iv) facilitate interoperability among various agents/humans.

http://www.educationaldatamining.org/EDM2008/uploads/proc/23_Abbas_15.pdf

That’s it, folks!