1. Comments on Natural Language and Argumentation Adam Wyner Department of Computer Science July 13, 2012 London Text Analytics Meetup

2. Overview Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/122 Problem statement. Representational layers: – Abstract argumentation. – Argumentation schemes. – Semi-automated argument analysis. – Well-formedness of argumentation schemes. – Contrast identification. Sketch the last three.

3. Problem Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/123 Arguments are everywhere. Arguments are expressed in natural language. Abstract arguments can be represented, related, and reasoned with formally and computationally in argumentation frameworks. Problem: How to get from arguments and contrasts from a corpus of natural language into an abstract representation in an argumentation framework?

4. Argument fragment for a camera Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/124

5. Pro and Con Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/125

6. Layers Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/126

7. Abstract argumentation Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/127

8. Input Graphs Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/128 http://rull.dbai.tuwien.ac.at:8080/ASPARTIX/index.faces

9. Output Extensions Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/129 Preferred Extension

10. Argument ladder (ArgMAS 2012) Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1210

11. Canonical sentences Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1211 Instantiation of the Position to Know Argumentation Scheme

12. Functional roles and typed propositional functions Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1212 An abstract argument variable is functionally tied to the propositions that represent the argumentation scheme, bridging the representational levels.

13. Question Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1213 How to systematically associate natural language expressions with an argumentation scheme so as to instantiate the scheme, then use it for reasoning?

14. Manual Argument Analysis Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1214 Coarse grained and uses no natural language processing.

15. Goals Extract arguments from source texts so they can be evaluated with formal automated tools. Speed the work of human analysts. Make argument identification more objective, systematic, structured, and amenable to development. Manual -> Semi-automatic support -> More semi- automatic support -> Fully automatic. Use aspects of NLP to incrementally address a range of problems (ambiguity, structure, contrasts,....) Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1215

16. Strategy and issues Decompose the complexity of a text – What are the parts of an argument? – How are the parts of the argument related? – What are the 'boundaries' of an argument? – What are the contrasts and negations from which we can derive attack relationships? – What kind of domain knowledge do we need? Take a rule-based approach. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1216

17. Use case: Which camera should I buy? Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1217

18. Value-based Practical Reasoning Argumentation Scheme Premises: Before doing action A, the current circumstances are R; After doing action A, the new circumstances are S; G is a goal of the agent Ag, where S implies G; Doing action A in R and achieving G promotes value V; Conclusion: We should perform action A. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1218

19. Consumer Argumentation Scheme Premises: Camera X has property P. Property P promotes value V for agent A. Conclusion: Agent A should Action1 Camera X. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1219

20. Critical questions Does Camera X have property P? Does property P promote value V for agent A? Is value V more important than value V’ for agent A? Answers can let presumptive conclusion remain or be challenged. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1220

21. Analyst’s goal: instantiate Premises: The Canon SX220 has good video quality. Good video quality promotes image quality for casual photographers. Conclusion: Casual photographers should buy the Canon SX220. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1221

22. … starting from this Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1222

23. Highlight parts of the argument Camera X has property P. Property P promotes value V for agent A. Value V is more important than value V’ for agent A. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1223

24. To find and instantiate the argument Argumentative indicators Property – with camera terminology Value for agent – with sentiment, user models Value V more important – with comparisons Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1224

25. Implementation with GATE Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1225

26. To find argument passages Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1226

27. Rhetorical terminology Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1227

28. To find what is being discussed Use domain terminology: – Has a flash – Number of megapixels – Scope of the zoom – Lens size – The warranty Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1228

29. Domain terminology Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1229

30. To find attacks between arguments Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1230

31. Sentiment terminology Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1231

32. Agents: user models User’s parameters Age, gender, education, previous camera experience,.... User’s context of use Party, indoors, sport, travel, desired output format,.... User’s constraints Cost, portability, size, richness or flexibility of features,.... User’s quality expectations Colour quality, information density, reliability,.... Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1232

33. Instantiating the CAS Premises: The Canon SX220 camera has property P. Property P promotes value V for agent A. Conclusion: Agent A should buy the Canon SX220. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1233

34. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1234

35. Query for patterns Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1235

36. An argument for buying the camera Premises: The pictures are perfectly exposed. The pictures are well-focused. No camera shake. Good video quality. Each of these properties promotes image quality. Conclusion: (You, the reader,) should buy the CanonSX220. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1236

37. An argument for NOT buying the camera Premises: The colour is poor when using the flash. The images are not crisp when using the flash. The flash causes a shadow. Each of these properties demotes image quality. Conclusion: (You, the reader,) should not buy the CanonSX220. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1237

38. Counterarguments to the premises of “Don’t buy” The colour is poor when using the flash. For good colour, use the colour setting, not the flash. The images are not crisp when using the flash. No need to use flash even in low light. The flash causes a shadow. There is a corrective video about the flash shadow. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1238

39. Locating argumentation schemes from text What is a well-formed argumentation scheme? Need to know in order to have some idea what textual indicators to look for in a corpus. An open question. Steps to address it (CMN 2012). Narrative coherence – rhetorical indicators, sentiment, negation, tense/aspect, roles,.... Corpus to work with. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1239

40. Preliminary work Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1240

41. How are contrasting pairs to be identified? Given a sentence and a corpus, find contrasting sentences. Compare sentences for textual similarity. Identify textual contrasts – negation, antonyms. – The value of budget is promoted. – The value of budget is not promoted. – The value of budget is demoted. Address diathesis, e.g. active and passive sentence forms – Bill returned the book. – The book was returned by Bill. – The book was not returned by Bill Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1241

42. How are contrasting pairs to be identified? Similarity measure (list comparison between sentences) using not just the text itself but also annotations for parts of speech and grammatical phrases. Find contrast indicators, e.g. ''not'', and tag for antonyms. Issues – scope, scale up, relate to similar work on textual inference and contradiction. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1242

43. Knowledge light v. heavy approaches Knowledge light in terms of knowledge of the domain or of language – statistical or machine learning approaches. Algorithmically compare and contrast large bodies of textual data, identifying regularities and similarities. Sparse data problem. Need a gold standard. No rules extracted. Opaque. Knowledge heavy - lists, rules, and processes. Labour and knowledge intensive. Transparent. Reasoning to annotation. Can do either. Depends what one wants. Finding what one knows in sparse data v. finding unknowns in rich data. 13/7/12 Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 43

44. Future work Tool refinement. Add domain and ontology modules to the tool. User models – how do they play a role? More complicated query patterns, what results do we get? More elaborate examples. Disambiguation issues for rhetorical terminology, e.g. when, because,.... Deal with it step-by-step to find how to disambiguate the indicators or other terminology. Further work on argumentation scheme characterisation. Further work on contrariness. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1244

45. Acknowledgements FP7-ICT-2009-4 Programme, IMPACT Project, Grant Agreement Number 247228. Collaborators: Jodi Schneider, Trevor Bench-Capon, Katie Atkinson, and Chenhui Lui. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1245

46. Thanks for your attention! Questions? Contacts: –Adam Wyneradam@wyner.info Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license 13/7/1246