Comments on Natural Language and Argumentation

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

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

3. Problem • 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? Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

25. Implementation with GATE • GATE “General Architecture for Text Engineering”. • Environment for text analysis. • Manual, semi-automatic, fully automatic. • Adds annotation to text: • Can work with large corpora of text • Coarse or fine-grained annotations • Rule-based or machine-learning. • Highlight annotations with colours • Search for and extract annotated text. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license

26. To find argument passages • Use: • Indicators of premise after, as, because, for, since, when, .... • Indicators of conclusion therefore, in conclusion, consequently, .... Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license

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

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

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

30. To find attacks between arguments • Use contrast terminology: • Indicators but, except, not, never, no, .... • Contrasting sentiment The flash worked poorly. The flash worked flawlessly. • Other contrast issues later. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license

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

32. Agents: user models • User’s parametersAge, gender, education, previous camera experience, .... • User’s context of useParty, indoors, sport, travel, desired output format, .... • User’s constraintsCost, 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

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

34. Domain properties, positive sentiment, premises Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license

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

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

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 notbuy the CanonSX220. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license

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

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

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

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

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

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. Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license

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

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

46. Thanks for your attention! • Questions? • Contacts: • Adam Wyner adam@wyner.info Wyner, Text Analytics Meetup, London, UK, (cc) by-nc-sa license