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Supporting Argumentative Discussions Management in the Web

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A support framework for community managers to help them in managing discussions based on NLP and argumentation theory

A support framework for community managers to help them in managing discussions based on NLP and argumentation theory

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  • 1. A Support Framework for ArgumentativeDiscussions Management in the WebElena Cabrio, Serena Villata, Fabien GandonWimmics TeamINRIA, I3S - Sophia Antipolis, France
  • 2. Supporting community managers usingNLP and argumentationCOMMUNITYMANAGERGOALEfficient management ofwiki pages by communitymanagers and animationsof communitiesE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 2
  • 3. Supporting community managers usingNLP and argumentationTEXTUALENTAILMENTTEXTUALENTAILMENTHow to detect the arguments,And the relationshipsamong them?1COMMUNITYMANAGERGOALEfficient management ofwiki pages by communitymanagers and animationsof communitiesE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 3
  • 4. Supporting community managers usingNLP and argumentationTEXTUALENTAILMENTTEXTUALENTAILMENTARGUMENTATIONTHEORYARGUMENTATIONTHEORYHow to detect the arguments,And the relationshipsamong them?1How to build the overallgraph of the changes anddiscover the winningarguments?2COMMUNITYMANAGERGOALEfficient management ofwiki pages by communitymanagers and animationsof communitiesE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 4
  • 5. Supporting community managers usingNLP and argumentationTEXTUALENTAILMENTTEXTUALENTAILMENTARGUMENTATIONTHEORYARGUMENTATIONTHEORYHow to detect the arguments,And the relationshipsamong them?1How to build the overallgraph of the changes anddiscover the winningarguments?2RDF/SPARQLRDF/SPARQL3COMMUNITYMANAGERHow to extractfurther insightfulinformation?GOALEfficient management ofwiki pages by communitymanagers and animationsof communitiesE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 5
  • 6. Outline1 Related literature2 Textual Entailment and Argumentation3 Combined Framework4 Experimental setting on Wikipedia revisions5 ConclusionsE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 6
  • 7. Related literature• Wikipedia revisions in NLP tasksZanzotto and Pennacchiotti (2010)Expanding textual entailment corpora from Wikipedia using co-trainingCabrio et al. (2012)Extracting context-rich entailment rules from wikipedia revision historyNelken and Yamangil (2008)Mining wikipedia revision histories for improving sentence compressionMax and Wisniewski (2010)Mining naturally-occurring corrections and paraphrases from wikipedia’s revisionhistoryDutrey et al. (2011)Local modifications and paraphrases in wikipedia’s revision history• Argumentation and NLPMoens et al. (2007)Automatic detection of arguments in legal textsCarenini and Moore (2006)Generating and evaluating evaluative argumentsWyner and van Engers (2010)A framework for enriched, controlled online discussion forums for e-governmentpolicy-makingHeras et al. (2010)How argumentation can enhance dialogues in social networksE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 7
  • 8. Related literature• Wikipedia revisions in NLP tasksZanzotto and Pennacchiotti (2010)Expanding textual entailment corpora from Wikipedia using co-trainingCabrio et al. (2012)Extracting context-rich entailment rules from wikipedia revision historyNelken and Yamangil (2008)Mining wikipedia revision histories for improving sentence compressionMax and Wisniewski (2010)Mining naturally-occurring corrections and paraphrases from wikipedia’s revisionhistoryDutrey et al. (2011)Local modifications and paraphrases in wikipedia’s revision history• Argumentation and NLPMoens et al. (2007)Automatic detection of arguments in legal textsCarenini and Moore (2006)Generating and evaluating evaluative argumentsWyner and van Engers (2010)A framework for enriched, controlled online discussion forums for e-governmentpolicy-makingHeras et al. (2010)How argumentation can enhance dialogues in social networksE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 8
  • 9. Textual Entailment• Generic framework for capturing major semantic inferenceneeds in NLP applications (Dagan and Glickman, 2004).• Relation between two textual fragments T and H:T ⇒ H: meaning of H can be inferred from meaning of T, asinterpreted by a typical language user.T (Wiki11): The land area of the contiguous United States is approximately1,800 million acres (7,300,000 km2)H (Wiki10): The land area of the contiguous United States is approximately1.9 billion acres (770 million hectares)T (Wiki10): The land area of the contiguous United States is approximately1.9 billion acres (770 million hectares)H (Wiki09): The total land area of the contiguous United States is approxima-tely 1.9 billion acres.E. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 9
  • 10. Textual Entailment• Generic framework for capturing major semantic inferenceneeds in NLP applications (Dagan and Glickman, 2004).• Relation between two textual fragments T and H:T ⇒ H: meaning of H can be inferred from meaning of T, asinterpreted by a typical language user.T (Wiki11): The land area of the contiguous United States is approximately1,800 million acres (7,300,000 km2)H (Wiki10): The land area of the contiguous United States is approximately1.9 billion acres (770 million hectares)T (Wiki10): The land area of the contiguous United States is approximately1.9 billion acres (770 million hectares)H (Wiki09): The total land area of the contiguous United States is approxima-tely 1.9 billion acres.E. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 10
  • 11. Abstract Argumentation Theory• Directed graph (Dung, 1995)Nodes: abstract argumentsEdges: attack relationargumentAargumentBargumentCargumentAargumentBIN OUT IN OUT INATTACK ATTACK ATTACK• Bipolar argumentation(Cayrol & Lagasquie-Schiex, 2005),(Boella et al., 2010)b ca a bc b caSupported attack Secondary attack Mediated attackE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 11
  • 12. Combined FrameworkWikipedia revisions for the article “United States”T (Wiki12): The land area of the contiguous United States is 2,959,064 square miles (7,663,941 km2).H (Wiki11): The land area of the contiguous United States is approximately 1,800 million acres(7,300,000 km2)T (Wiki11): The land area of the contiguous United States is approximately 1,800 million acres(7,300,000 km2)H (Wiki10): The land area of the contiguous United States is approximately 1.9 billion acres )(770 million hectares)T (Wiki10): The land area of the contiguous United States is approximately 1.9 billion acres(770 million hectares)H (Wiki09): The total land area of the contiguous United States is approximately 1.9 billion acres.A2Wiki10A3Wiki11A1Wiki09A4Wiki12(a)A2Wiki10A3Wiki11A1Wiki09A4Wiki12(b)E. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 12
  • 13. Revisions in RDF usingSIOC-Argumentation extended vocabularyE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 13
  • 14. Revisions in RDF usingSIOC-Argumentation extended vocabularyE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 14
  • 15. Extracting further informationfrom revisions in RDFE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 15
  • 16. Experimental setting:preprocessing Wikipedia dumps• 4 dumps of English Wikipedia (2009, 2010, 2011, 2012)• 5 most revised pages: United States, World War II, GeorgeBush, Michael Jackson, Britney SpearsE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 16
  • 17. Experimental setting:extraction of entailment pairs• Documents are sentence splitted, and sentences are aligned• To measure the similarity between the sentences: PositionIndependent Word Error Rate (PER) [Tillman et al., 1997]• Different thresholds are set to cluster pairs into different sets• Sentences with major editing are selected (0.2<PER<0.6)• TE pair: revised sentence as T, original sentence as HEntailment No EntailmentTraining Set 114 pairs 114 pairsTest Set 101 pairs 123 pairsE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 17
  • 18. Experimental setting: evaluation• EDITS system (Edit Distance Textual Entailment Suite)(Kouylekov and Negri, 2010), off-the-shelf systemBasic configuration: word overlap and cosine similarityalgorithms; distance calculated on lemmas; stopword list• FIRST STEP: TEXTUAL ENTAILMENTTrain TestEDITS configurations rel Precision Recall Accuracy Precision Recall AccuracyWordOverlapyes 0.83 0.820.830.83 0.820.78no 0.76 0.73 0.79 0.82CosineSimilarityyes 0.58 0.890.630.52 0.870.58no 0.77 0.37 0.76 0.34• SECOND STEP: TE+ARGUMENTATION THEORYTestConfiguration Precision Recall F-measureWordOverlap + AT 0.90 0.92 0.91E. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 18
  • 19. Experimental setting: evaluation• EDITS system (Edit Distance Textual Entailment Suite)(Kouylekov and Negri, 2010), off-the-shelf systemBasic configuration: word overlap and cosine similarityalgorithms; distance calculated on lemmas; stopword list• FIRST STEP: TEXTUAL ENTAILMENTTrain TestEDITS configurations rel Precision Recall Accuracy Precision Recall AccuracyWordOverlapyes 0.83 0.820.830.83 0.820.78no 0.76 0.73 0.79 0.82CosineSimilarityyes 0.58 0.890.630.52 0.870.58no 0.77 0.37 0.76 0.34• SECOND STEP: TE+ARGUMENTATION THEORYTestConfiguration Precision Recall F-measureWordOverlap + AT 0.90 0.92 0.91E. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 19
  • 20. Experimental setting: evaluation• EDITS system (Edit Distance Textual Entailment Suite)(Kouylekov and Negri, 2010), off-the-shelf systemBasic configuration: word overlap and cosine similarityalgorithms; distance calculated on lemmas; stopword list• FIRST STEP: TEXTUAL ENTAILMENTTrain TestEDITS configurations rel Precision Recall Accuracy Precision Recall AccuracyWordOverlapyes 0.83 0.820.830.83 0.820.78no 0.76 0.73 0.79 0.82CosineSimilarityyes 0.58 0.890.630.52 0.870.58no 0.77 0.37 0.76 0.34• SECOND STEP: TE+ARGUMENTATION THEORYTestConfiguration Precision Recall F-measureWordOverlap + AT 0.90 0.92 0.91E. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 20
  • 21. 1 Connect users to their arguments in online communities2 Arguments’ evaluation depending on sources’ expertise3 TE three-way judgement task:entailment, contradiction, unknownE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 21
  • 22. Thanks for your attention!http://bit.ly/WikipediaDatasetXMLhttp://bit.ly/WikipediaDatasetRDFhttp://bit.ly/SIOC_ArgumentationE. Cabrio, S. Villata, F. Gandon, Argumentative Discussions Management in the Web. 22

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