comments on natural language and argumentation adam wyner department of computer science july 13,...

Comments on Natural Language and Argumentation

Adam WynerDepartment of Computer Science

July 13, 2012

London Text Analytics Meetup

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

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Overview

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• Problem statement.• Representational layers:

– Abstract argumentation.– Argumentation schemes.– Semi-automated argument analysis.– Well-formedness of argumentation schemes.– Contrast identification.

• Sketch the last three.


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Problem

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• 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?


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Argument fragment for a camera

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Pro and Con

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Layers

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Abstract argumentation

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Input Graphs

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http://rull.dbai.tuwien.ac.at:8080/ASPARTIX/index.faces


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Output Extensions

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Preferred Extension


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Argument ladder (ArgMAS 2012)

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Canonical sentences

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Instantiation of the Position to Know Argumentation Scheme


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Functional roles and typed propositional functions

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An abstract argument variable is functionally tied to the propositions that represent the argumentation scheme, bridging the representational levels.


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Question

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How to systematically associate natural language expressions with an argumentation scheme so as to instantiate the scheme, then use it for reasoning?


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Manual Argument Analysis

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Coarse grained and uses no natural language processing.


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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,....)

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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.

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Use case: Which camera should I buy?

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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.

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Consumer Argumentation Scheme

Premises: • Camera X has property P.• Property P promotes value V for agent A.

Conclusion: • Agent A should Action1 Camera X.

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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.

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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.

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… starting from this

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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.

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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

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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.

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To find argument passages

• Use:– Indicators of premise after, as, because, for, since, when, .... – Indicators of conclusion therefore, in conclusion, consequently, ....

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Rhetorical terminology

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To find what is being discussed

• Use domain terminology:– Has a flash– Number of megapixels– Scope of the zoom– Lens size– The warranty

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Domain terminology

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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.

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Sentiment terminology

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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, ....

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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.

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Domain properties, positive sentiment,

premises

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Query for patterns

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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.

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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.

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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.

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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.

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Preliminary work

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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

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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.

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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.

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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.

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Acknowledgements

• FP7-ICT-2009-4 Programme, IMPACT Project, Grant Agreement Number 247228.

• Collaborators: Jodi Schneider, Trevor Bench-Capon, Katie Atkinson, and Chenhui Lui.

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Thanks for your attention!

• Questions?• Contacts:

– Adam Wyner [email protected]

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comments on natural language and argumentation adam wyner department of computer science july 13,...

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