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Natural Language Processing of Argumentation. Adam Wyner. Summer School Lecture 2014

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Adam Wyner's presentation on natural language processing of argumentation such as found in social media, newspapers, and law. Relevant to semantic web, text analysis, computational linguistics, argumentation. University of Aberdeen.

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Natural Language Processing of Argumentation. Adam Wyner. Summer School Lecture 2014

  1. 1. Argument Extrac.on from Social Media Using GATE Adam Wyner Compu.ng Science, University of Aberdeen Summer School on Argumenta.on: Computa.onal and Linguis.c Perspec.ves University of Dundee Sept. 7, 2014
  2. 2. Goals Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen • Iden.fy materials (social media) and generic issues. • Outline linguis.c issues. • Outline GATE methodology. • Provide some examples. 2
  3. 3. Materials Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 3
  4. 4. Where Arguments Appear Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 4 • Consumer websites: Amazon, eBay,... • Law: policy making, Supreme Court transcripts, case based reasoning, regula.ons. • BBC's Have Your Say and Moral Maze. • Medical diagnosis. • Current events. • Making plans. • Debatepedia, Wikipedia, mee.ng annota.ons, web-­‐forums,... • Social media: Facebook, da.ng
  5. 5. Current Events Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen • ScoZsh Independence and Currency • h[p://www.bbc.co.uk/news/uk-­‐scotland-­‐scotland-­‐ poli.cs-­‐2622589 5
  6. 6. ScoZsh Independence 2014 The issue of what currency an independent Scotland would use has become the key ba[leground of the referendum debate. The ScoZsh government is in favour of a sterling zone, saying it would be in the interests of both Scotland and the UK to con.nue to formally share the pound if the former votes for independence, ensuring stability for both states. UK chancellor George Osborne has said the UK would not enter into a currency union with Scotland if it voted 'Yes' in September's referendum, claiming such a union would be against the economic interests of England, Wales and Northern Ireland. Mr Osborne's statement was the UK government's strongest interven.on in the debate yet, and his posi.on was supported by both Labour and the Liberal Democrats. First Minister Alex Salmond countered Mr Osborne's claims in a speech to pro-­‐independence business leaders in Aberdeen on Monday, which he said had "deconstructed" the case against a currency union. So what are Mr Osborne's key arguments and how has Mr Salmond sought to counter them? Claim: Trade with Scotland is important to the UK, but the overall propor;on is small George Osborne: "I'm the first to say that our deeply integrated businesses and their suppliers are compelling reasons for keeping the UK together -­‐ 70% of ScoZsh trade is with the rest of the UK. That is a massive propor.on. "And trade with Scotland is important to the rest of the UK -­‐ but at only 10% of the total trade, it is a much smaller propor.on. These trade figures don't make the unanswerable case for a shared currency that the ScoZsh government assume." Alex Salmond: "I am publishing an es.mate of the transac.ons costs he would poten.ally impose on businesses in the rest of the UK. They run to many hundreds of millions of pounds. My submission is that this charge -­‐ let us call it the George tax -­‐ would be impossible to sell to English business. "In fact if you remove oil and gas from the equa.on, Scotland is one of the very few countries in the world with which England has a balance of trade surplus." Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 6
  7. 7. Arguments in debategraph.org Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen Current Method -­‐ read text -­‐ manually analyse -­‐ manually enter text into tool -­‐ manually annotate. Problems -­‐ slow, costly, error-­‐ prone, ad hoc, must search for 'place' of new addi.ons, etc.... 7
  8. 8. Consumer Comments on Amazon Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 8
  9. 9. Pro and Con Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 9
  10. 10. Comments on Comments Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 10
  11. 11. Policy Consulta.ons -­‐ LIBER on Copyright -­‐ Ques;on 9. Should the law be clarified with respect to whether the scanning of works held in libraries for the purpose of making their content searchable on the Internet goes beyond the scope of current excep;ons to copyright? -­‐ Yes. -­‐ Not all the material digi.sed by publishers is scanned with OCR (Op.cal Character Recogni.on) with the purpose of making the resul.ng content searchable. If the rights holders will not do this, libraries should be able to offer this service. It would have a transforma.ve effect on research, learning and teaching by opening up a mass of content to users which can be searched using search engines. The interests of copyright holders will not be harmed, because the resul.ng output will act as marke.ng material for their materials. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 11
  12. 12. What Needs to be Done? Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen • Annotate textual passages for argument relevant por.ons (premise, claim) • Annotate rela.ons amongst passages (premise of what argument) • Represent in some machine readable form. • Thought experiments to objec7fy and abstract the issues. 12
  13. 13. Generically What Needs to be Done? Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 13
  14. 14. What Needs to be Done? Basic Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 14 andalka nadlka fa adlkaa la lkd alkdj a a akj dal;k fda ada. a;lkd a andalkda anda;k a jad ie ae. a;lkd. nainea ; alkei nai lalin oa nekn. ake anoiiena dk aieane0-­‐a an a;kl aeu ajena. ;oi anoi alkd ao;na oen oiana oin. l ;kja dka j ajda djflka kle ak kad la ien ae n en. lkj ad ad fa ;adja dfakd. Source Text A a1.p.1. -­‐ andalka nadlka fa adlkaa la lkd alkdj a a akj dal;k fda ada. a1.p.2. -­‐ a;lkd a andalkda anda;k a jad ie ae. a;lkd. a1.c. -­‐ nainea ; alkei nai lalin oa nekn. a2.p.1 -­‐ ake anoiiena dk aieane0-­‐a an a;kl aeu ajena. a2.p.1 -­‐ ;oi anoi alkd ao;na oen oiana oin. a2.e.3 -­‐ l ;kja dka j ajda djflka kle ak kad la ien ae n en. a1.c -­‐ lkj ad ad fa ;adja dfakd. Annotated Text A Key: premise, excep.on, claim
  15. 15. What Needs to be Done? Ques.ons Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen • How do we know (as readers) which is a premise, which is a claim, and which is an excep.on? – explicit linguis.c markers (e.g. assuming X, therefore Y) – order of sentences? – other, e.g. context? • If we scrambled the order of the sentences, could we recons.tute the argument annota.on? – Engineer – "Doesn't happen, not relevant. Build for par.culars." – Scien.st – "Does it happen? If it does or could, how do we address it? Explore for principles." 15
  16. 16. What Needs to be Done? Scramble Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 16 andalka nadlka fa adlkaa la lkd alkdj a a akj dal;k fda ada. nainea ; alkei nai lalin oa nekn. a;lkd a andalkda anda;k a jad ie ae. a;lkd. ;oi anoi alkd ao;na oen oiana oin. lkj ad ad fa ;adja dfakd. l ;kja dka j ajda djflka kle ak kad la ien ae n en. ake anoiiena dk aieane0-­‐a an a;kl aeu ajena. Source Text A a1.p.1. -­‐ andalka nadlka fa adlkaa la lkd alkdj a a akj dal;k fda ada. a1.p.2. -­‐ a;lkd a andalkda anda;k a jad ie ae. a;lkd. a1.c. -­‐ nainea ; alkei nai lalin oa nekn. a2.p.1 -­‐ ake anoiiena dk aieane0-­‐a an a;kl aeu ajena. a2.p.1 -­‐ ;oi anoi alkd ao;na oen oiana oin. a2.e.3 -­‐ l ;kja dka j ajda djflka kle ak kad la ien ae n en. a1.c -­‐ lkj ad ad fa ;adja dfakd. Annotated Text A Key: premise, excep.on, claim
  17. 17. Scramble in Comment Update Argumenta.on Summer School, Dundee nada dnana a kkkd andai ;a. n=jja nmae a;kda nIanl. 07/09/2014 A. Wyner, Univ of Aberdeen 17 andalka nadlka fa adlkaa la lkd alkdj a a akj dal;k fda ada. a;lkd a andalkda anda;k a jad ie ae. a;lkd. nainea ; alkei nai lalin oa nekn. Source Text A a1.p.1. -­‐ andalka nadlka fa adlkaa la lkd alkdj a a akj dal;k fda ada. a1.p.2. -­‐ a;lkd a andalkda anda;k a jad ie ae. a;lkd. a1.p.3. -­‐ n=jja nmae a;kda nIanl. a1.e.2. -­‐ nada dnana a kkkd andai ;a. a1.c. -­‐ nainea ; alkei nai lalin oa nekn. Annotated Text A plus Key: premise, excep.on, claim Source Text B Source Text C a;lkd a andalkda likalaka anda;k a jad ie ae. a;lkd. (contrary to a1.p.2) Source Text D
  18. 18. Scrambling Ques.ons Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen • How do we know when two premises are/are not the same? • How do we know what argument to a[ach a proposi.on to? • Addressing these ques.ons may require some deep syntac.c and seman.c analysis (hint, I think it does and can be done....eventually). • BUT VERY HARD!! • Find a less demanding, near term approach towards similar objec.ves. 18
  19. 19. Generic Issues Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen • Reconstruc.on of arguments from textual sources: – extrac.on for argument evalua.on. – Discon.nuity of arguments in textual source. – Knowledge base construc.on and dynamics. • Linguis.c issues: – Domain terminology. – Linguis.c informa.on and variety (many-­‐to-­‐one sentence-­‐ proposi.on). – Argument rela.ons (premise, claim, excep.on, contrary). – Sources of defeasibility (epistemic 'strength'). – Other argument component, e.g. proposi.onal aZtudes (e.g believe, know), speech act verbs (e.g. assert, grant). 19
  20. 20. Argument Pipeline Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 20
  21. 21. Loca.ng the Problem and Engineering a Solu.on Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 21 • The knowledge acquisi.on bo[leneck from NL to some formal representa.on. • Rela.onship to other parts of the argumenta7on processing pipeline.
  22. 22. Three Stages Graph – Structured or Instan.ated AFs gOkZI[jjQ][ gZIq]gX Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 22 []qYIGOI
  23. 23. hI rjI[hQ][h]N gOkZI[jh rjI[hQ][h]N ][EYkhQ][h /jIdE][hjgkEj gOkZI[jh[GjjEXh /jIdÏQGI[jQNshIjh]N EEIdjIGgOkZI[jh /jIdďQGI[jQNshIjh]N EEIdjIGE][EYkhQ][h Three Stages -­‐ Caminada and Wu 2011 Knowledge Acquisi.on Bo[leneck: .me, labour, exper.se to construct a KB at scale.
  24. 24. Logic-­‐based Instan.ated Argumenta.on Besnard and Hunter Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 23 • An argument is an ordered pair ψ, α; ψ is a subset of a given KB and α is an atomic proposi.on from the KB; ψ is a minimal set of formulae such that ψ implies α, and ψ does not imply a contradic.on. ψ is said to support the claim α. • Where p and q are atoms, and where the KB is comprised of p and p→q, then {p, p→q}, q is an argument. • We could have a KB from which we can form an argument which supports ¬q, {p, p→¬q}, ¬q. In addi.on and with respect to this argument, suppose we can form an undercuer {r, r→¬p}, ¬p and a rebual {r, r→¬p, ¬p→q}, q}. • KBs (even rela.vely small ones) generate lots of arguments and a[ack rela.onships which can be structured in a tree.
  25. 25. Abstract Argumenta.on Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 24 Preferred extension: {a, c, d, h, i, k}
  26. 26. Zeroing In Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen Source text Knowledge base argumenta.on schemes Generated arguments (abstract or instan.ated). 25
  27. 27. Context with Respect to Analysis and Argumenta.on Schemes Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 26
  28. 28. Current Tools to Extract and Structure Arguments from Text Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 27 • Ra.onale, Araucaria, Carneades (Gordon 2007), IMPACT Project, Legal Appren.ce, Argument Wall,.... • Pale[e of annota.ons and templates. • All manual. No NLP.
  29. 29. Argumenta.on Schemes Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen • Pa[erns of presump.ve (defeasible) reasoning (Walton 1996) • Prac.cal Reasoning with values: – Do ac.on (transi.on) because: • Current circumstances -­‐ a list of literals. • Consequences – a list of literals. • Values (promoted, demoted, neutral wrt ac.ons) – a list of terms. • Credible Source: – Z is accepted because: • X is an expert in domain Y. • X stated literal Z • Z is about domain Y. 28
  30. 30. Overall Proposal Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen • Normalise natural language source material into argumenta.on schemes. • Formalise argumenta.on schemes in terms of roles of proposi.ons in the scheme and internal structure of proposi.ons (predicates and typed variables). • Connect argumenta.on schemes to abstract arguments. • Relate one scheme to another in terms of contrariness. • Extract scheme relevant informa.on from the source. • Create a knowledge base to instan.ate variables. 29
  31. 31. Caveat Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen • Low level automa.on, using high level structures as guides. • For example, no automa.c search for scheme filling, grounding of variables, contrast iden.fica.on. • Progress can be made on these (and for contrast iden.fica.on, there is significant work already). 30
  32. 32. Normalise for Argumenta.on Schemes Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 31
  33. 33. Annotate – Query – Extract Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 32 • Annotate with respect to Argumenta.on Schemes. – characteris.c terminology of the scheme. – generalise the terminology to cover varia.on. – dis.nguish domain from generic terminology. • Complex, flexible queries over the annota.ons. – Low level (atomic) and high level (molecular) construc.ons. – Interac.ve, semi-­‐automa.c. • Export to some machine readable format -­‐ XML.
  34. 34. Language Issues Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 33
  35. 35. Problems with Language I • Iden.fica.on, implicit informa.on, mul.ple forms with the same meaning, the same form with mul.ple meanings: • En.ty ID: Jane Smith, for plain.ff. • Rela.on ID: Edgar Wilson disclosed the formula to Mary Hays. • Bill drove the car into Phil at 60 MPH. (agent, instrument, killing) • Jane Smith, Jane R. Smith, Smith, A[orney Smith.... • Jane Smith in one case decision need not be the same Jane Smith in another case decision. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 34
  36. 36. Problems with Language II • Concepts, dispersed meanings, rules, diathesis: • Plain.ff, judge, a[orney. • Jane Smith represented Jones Inc. She is a partner at Dewey, Chetum, and Howe. To contact her, write to j.smith@dch.com. • If a woman is over 62 years old and lives in the UK, she is a pensioner. • Diathesis: alterna.ve sentence forms with (almost) synonymous meaning: Bill pushed Jill; Jill was pushed by Bill. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 35
  37. 37. Problems with Language III • Ambiguity, vagueness, underspecifica.on: • The man saw the woman with binoculars. • It is illegal to leave a heap of shoes on the sidewalk. • Vehicles may not be driven in the park. • Sarcasm, irony. • Interpreta.on. • Context dependence, subjec.vity, arbitrary meaning, when I was at school, I know language.... Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 36
  38. 38. Problems with Language IV • Complexity, length, and layout (see our Camera example). • Intersenten.al connec.ons: • Bill le the house. He drove home. • Bill le the house. He didn't feel comfortable there. • Bill le the house. It was an old house, once owned by a wealthy merchant. • Synonymy, antonyms, meronyms (finger part of hand), etc. • Repe..on. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 37
  39. 39. Problems for Annota.on • Annotate large legacy corpora. • Address growth of corpora. • Reduce number of human annotators and tedious work. • Make annota.on systema.c, automa.c, and consistent. • Annotate fine-­‐grained informa.on: • Names, loca.ons, addresses, web links, organisa.ons, ac.ons, argument structures, rela.ons between en..es. • Map from well-­‐draed documents in NL to RDF/OWL/XML. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 38
  40. 40. Addressing the Problems • Decompose big problems down to smaller problems. • Modularise problems. • Address the smaller, modular problems. • Compose solu.ons from parts. • Iden.fy (set aside, address, assign to someone else) remaining and/or highly problema.c issues. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 39
  41. 41. Methodology Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 40
  42. 42. Approaches • Knowledge light in terms of knowledge of the domain or of language – sta.s.cal or machine learning approaches. Algorithmically compare and contrast large bodies of textual data, iden.fying regulari.es and similari.es. Sparse data problem. Need a ‘gold standard’. No rules extracted. Opaque. Hard to modify. • Knowledge heavy in terms of lists, rules, and processes. Labour and knowledge intensive. Creates gold standards. Transparent. Can jus.fy outcomes. Can 'correct' solu.ons. • Can do either. Where textual traceability (jus.fica.on) is essen.al, knowledge heavy is important. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 41
  43. 43. Overall Approach • Decompose large complex problems into smaller, manageable problems for which we can create solu.ons. • Soware engineering approach. • Papers by Wyner and Peters (2010, 2011). Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 42
  44. 44. Development Caveat • Developing working prototypes (much less public and/or commercial tools) takes resources. • Tool development • Corpus development • Language analysis • It is a slow, painstaking, and gradual process of construc.ng modules to do the small tasks you need to build the large applica.ons you want. • Not a simple phone app. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 43
  45. 45. Development Cycle Source Text Linguis.c Analysis Tool Construc.on Evalua.on Knowledge Extrac.on Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 44
  46. 46. Whazza Methodology? 07/09/2014 Argumenta.on Summer School, Dundee A. Wyner, Univ of Aberdeen 45
  47. 47. Linguis.c Processing Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 46
  48. 48. Computa.onal Linguis.c Cascade I • Sentence segmenta.on -­‐ divide text into sentences. • Tokenisa.on -­‐ words iden.fied by spaces between them. • Part of speech tagging -­‐ noun, verb, adjec.ve.... • Morphological analysis -­‐ singular/plural, tense, nominalisa.on, ... • Shallow syntac.c parsing/chunking -­‐ noun phrase, verb phrase, subordinate clause, .... • Named en.ty recogni.on -­‐ the en..es in the text. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 47
  49. 49. Computa.onal Linguis.c Cascade II • Dependency analysis – sentence subject, subordinate clauses, pronominal anaphora,... • Rela.onship recogni.on – X is president of Y; A hit B with a car and killed B. • Enrichment -­‐ add lexical seman.c informa.on to verbs or nouns. • Supertagging – adding conceptual annota.ons to text. • Transla.on to logic for reasoning. • Each step guided by pa[ern matching and rule applica.on. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 48
  50. 50. Overall Processing Strategy • Make implicit informa.on explicit by adding machine readable annota7ons. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 49
  51. 51. A Tool Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 50
  52. 52. GATE • General Architecture for Text Engineering (GATE) -­‐ open source framework which supports plug-­‐in NLP components to process a corpus of text. • GATE Training Courses h[ps://gate.ac.uk/ • A GUI to work with the tools. • A Java library to develop further applica.ons. • Components and sequences of processes, each process feeding the next in a “pipeline”. • Annotated text output or other sorts of output. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 51
  53. 53. GATE Benefits Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 52 • No need for parsed, pre-­‐structured text. • Generic components apply anywhere. • No need for a gold standard. • Low entry point, no programming required. • Useful interface for analysis and demonstra.on. • Lots of public resources and open to build more add-­‐ons. • Connects to other tools, widely used....
  54. 54. GATE Basic Process Flow Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 53 Can add further processing components to pipeline, e.g. NER, co-­‐reference, other other annota.ons,...
  55. 55. GATE -­‐ Gaze[eers • Gaze[eers are lookup lists that add features -­‐ when a string in the text is located in a lookup list, annotate the string in the text with the feature. Conceptual covers. • Feature: list of items... • Obliga.on: ought, must, obliged, obliga.on.... • Excep.on: unless, except, but, apart from.... • Verbs according to thema.c roles: lists of verbs and their associated roles, e.g. run has an agent (Bill ran), rise has a theme (The wind blew). 07/09/2014 Argumenta.on Summer School, Dundee A. Wyner, Univ of Aberdeen 54
  56. 56. GATE – JAPE Rules • JAPE Rules (finite state transduc.on rules) create overt annota.ons and reuse other annota.ons (e.g. Parser Output): 07/09/2014 Argumenta.on Summer School, Dundee A. Wyner, Univ of Aberdeen 55
  57. 57. GATE – Building an Applica.on • Have Gaze[eer lists and JAPE rules for: • lists in various forms; • excep.on phrases in various forms; • condi.onals in various forms; • deon.c terms; • associa.ng gramma.cal roles (e.g. subject and object) with thema.c roles (agent and theme) in various forms. 07/09/2014 Argumenta.on Summer School, Dundee A. Wyner, Univ of Aberdeen 56
  58. 58. Example -­‐ Camera Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 57
  59. 59. Argument Fragment for a Camera Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 58
  60. 60. Pro and Con Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 59
  61. 61. Comments on Comments Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 60
  62. 62. Goals • Extract arguments distributed across a corpora and evaluate them with formal, automated tools. • Speed the work of human analysts. • Provide semi-­‐automa3c support. • Use aspects of NLP to incrementally address a range of problems (ambiguity, structure, contrasts,....) • Wyner, Schneider, Atkinson, and Bench-­‐Capon (2012). Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 61
  63. 63. Consumer Argumenta.on Scheme Variables in schemes as targets for extrac7on. Premises: • Camera X has property P. • Property P promotes value V for agent A. Conclusion: • Agent A should Ac;on Camera X. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 62
  64. 64. Analyst’s Goal: Instan.ate 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. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 63
  65. 65. Annota.ng Text • Annotate text: – Simple or complex annota.ons. – Highlight annota.ons with – Search for and extract text by annota.on. • GATE “General Architecture for Text Engineering”. – Works with large corpora of text. – Rule-­‐based or machine-­‐learning approaches. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 64
  66. 66. To Find Argument Passages • Use: – Indicators of aJer, as, because, for, since, when, .... – Indicators of therefore, in conclusion, consequently, .... Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 65
  67. 67. Rhetorical Terminology Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 66
  68. 68. To Find What is Being Discussed • Use : – Has a flash – Number of megapixels – Scope of the zoom – Lens size – The warranty Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 67
  69. 69. Domain Terminology Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 68
  70. 70. To Find A[acks Between Arguments • Use contrast terminology: – Indicators but, except, not, never, no, .... – Contras.ng sen.ment The flash worked . The flash worked . Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 69
  71. 71. Sen.ment Terminology Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 70
  72. 72. , , Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 71
  73. 73. Query for Pa[erns Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 72
  74. 74. 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 proper.es promotes image quality. Conclusion: (You, the reader,) should buy the CanonSX220. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 73
  75. 75. 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 proper.es demotes image quality. ! Conclusion: (You, the reader,) should not buy the CanonSX220. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 74
  76. 76. Counterarguments to the Premises of “Don’t buy” The colour is poor when using the flash. For good colour, use the colour seZng, 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 correc.ve video about the flash shadow. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 75
  77. 77. In More Detail Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 76
  78. 78. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 77
  79. 79. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 78
  80. 80. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 79
  81. 81. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 80
  82. 82. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 81
  83. 83. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 82
  84. 84. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 83
  85. 85. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 84
  86. 86. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 85
  87. 87. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 86
  88. 88. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 87
  89. 89. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 88
  90. 90. Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 89
  91. 91. ANNIC Movie Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 90
  92. 92. Example -­‐ Rules • Rule iden.fica.on in regula.ons; what one can 'argue' for and against. • Using previous modules. • Wyner and Peters (2011) Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 91
  93. 93. Sample Outputs Consequence, list structure, and conjuncts of the antecedent. Excep.on, agent NP, deon.c concept, ac.ve main verb, theme. 07/09/2014 Argumenta.on Summer School, Dundee A. Wyner, Univ of Aberdeen 92
  94. 94. Sample Output Theme, deon.c modal, passive verb, agent with complex rela.ve clause. 07/09/2014 Argumenta.on Summer School, Dundee A. Wyner, Univ of Aberdeen 93
  95. 95. Sample Output -­‐ Overall 07/09/2014 Argumenta.on Summer School, Dundee A. Wyner, Univ of Aberdeen 94
  96. 96. Sample Output -­‐ XML 07/09/2014 Argumenta.on Summer School, Dundee A. Wyner, Univ of Aberdeen 95 This is an inline representa.on, and not 'pure' XML as tags can overlap. There is also offset, which can be modified easily.
  97. 97. Sample Output – ANNIC Search 07/09/2014 Argumenta.on Summer School, Dundee A. Wyner, Univ of Aberdeen 96
  98. 98. Gold Standards Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 97
  99. 99. Teamware to Create Gold Standards 07/09/2014 Argumenta.on Summer School, Dundee A. Wyner, Univ of Aberdeen 98
  100. 100. Results of Annota.on • The annotators carry out their task and complete the project. • Carry out inter-­‐annotator agreement analysis. • Curate the disagreements to create a Gold Standard corpus. Can use this for machine learning. • Search the annota.ons using an online tool, e.g. ANNIC. 07/09/2014 Argumenta.on Summer School, Dundee A. Wyner, Univ of Aberdeen 99
  101. 101. Addi.ons Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 100
  102. 102. Add to Explorer (or Teamware) • Verbs for proposi.onal aZtudes, e.g. believe, know, hope and speech acts, e.g. stated, men7oned, guessed. • Opinion adverbials -­‐ obviously, so far as I know, scien7fically. • Ques.on words and markers – who, why, ? • Rhetorical connec.ves -­‐ elabora7on, example, contrast. • Others.... Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 101
  103. 103. References Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen • Wyner, van Engers, Hunter (2010) • Wyner and Peters (2010, 2011) • Wyner, Schneider, Atkinson, and Bench-­‐Capon (2012) 102
  104. 104. Thanks for your a[en.on! • Questions? • Contacts: – Adam Wyner adam@wyner.info Argumenta.on Summer School, Dundee 07/09/2014 A. Wyner, Univ of Aberdeen 103

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