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UAIC Participation at RTE4 Adrian Iftene [email_address] „ Al. I. Cuza“ University, Iasi, Romania Faculty of Computer Science
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
RTE Competition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
System presentation Initial   data Minipar module Final result NE Rule Negation  Rules Acronyms Background knowledge eXtended  Wordnet GATE VerbOcean Numbers  & Dates Contradiction Rules In RTE-3 competition Two-way 69.13 % Three-way 54.7 % After RTE-3 competition Test Data 2007: Two-way 74.1 % Three-way 71.8 % Test Data 2008 : Two-way 72.1 % Three-way 68.5 % Fitness Rule Main module TreeTagger Resources DIRT Dependency trees for  (T, H) pairs LingPipe module Named entities for  (T, H) pairs Wikipedia
Example NE (N) subj loc adv (A) subj mod … … NE (N) loc … … DIRT, VerbOcean WordNet Acronyms, BK DIRT: solve=resolve 0.31453 DIRT: convict=arrest 0.28895 DIRT:  convict=acquit  0.302455,OPPOSITE VerbOcean:  increase<>decrease,  VerbOcean:   leave<>stay WordNet: trouble=problem WordNet: talk=discussion   Acronym: EU=European Union BK: Buenos Aires [in] Argentine BK: 16 [is] sixteen   Hypothesis Text verb H  (V) noun (N) verb T  (V) noun (N) … … … …
Rules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Entailment cases ,[object Object],[object Object],[object Object],[object Object],[object Object],70 80
Entailment cases (cont…) ,[object Object],[object Object],[object Object],[object Object],[object Object]
No entailment cases ,[object Object],[object Object],[object Object]
Contradiction cases ,[object Object],[object Object],[object Object],[object Object]
Contradiction cases (cont…) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Contradiction cases (cont…) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Contradiction cases (cont…) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Unknown cases ,[object Object],[object Object],[object Object]
Unknown cases (cont…) ,[object Object],[object Object],[object Object],[object Object]
Unknown cases (cont…) ,[object Object],[object Object],[object Object],[object Object]
Unknown cases (cont…) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Results in RTE4 68.50% 1000 685 1000 Total 54.25% 42.86% 73.89% 203 150 350 Unknown 58.72% 46.00% 81.18% 85 69 150 Contradiction 76.90% 93.20% 65.45% 712 466 500 Entailment F-measure Recall Precision Total offered by our system Correct offered by our system In Gold Answer Type 72.10% 1000 721 1000 Total 64.72% 51.00% 88.54% 288 255 500 No 76.90% 93.20% 65.45% 712 466 500 Yes F-measure Recall Precision Total offered by our system Correct offered by our system In Gold Answer Type
Comparison with RTE-3 To be able to see each component’s relevance, the systems were run in turn with each component removed 5.55 % 68.10 % - - Without Contradiction rule 7.21 % 66.90 % 16.71 % 57.58 % Without NE rule 4.72 % 68.70 % 2.17 % 67.63 % Without Negation rule 2.36 % 70.40 % 2.00 % 67.75 % Without BK 0.42 % 71.80 % 1.08 % 68.38 %  Without Acronyms 4.16 % 69.10 % 1.63 % 68.00 % Without WordNet 0.97 % 71.40 % 0.54 % 68.76 % Without DIRT Relevance Precision  Relevance Precision  RTE-4 RTE-3 System Description
Limits of the system ,[object Object],[object Object],[object Object],[object Object],[object Object]
Limits of the system (cont…) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Limits of the system (cont…) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Applications ,[object Object],[object Object]
Question Answering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
QA: System architecture ,[object Object],[object Object],[object Object],[object Object]
Results in QA Competitions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
AVE competition ,[object Object],[object Object],[object Object],[object Object]
The AVE system ,[object Object],[object Object],[object Object],65 % Alicante University, Spain 70 % ” Al. I. Cuza” University, Romania 70 % DFKI, Germany QA accuracy Group 62.96 % Alicante University, Spain 70.37 % ” Al. I. Cuza” University, Romania 70.37 % DFKI, Germany QA accuracy Group
Conclusions ,[object Object],[object Object],[object Object],[object Object]
Acknowledgments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]
References ,[object Object]

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UAIC Participation at RTE4

  • 1. UAIC Participation at RTE4 Adrian Iftene [email_address] „ Al. I. Cuza“ University, Iasi, Romania Faculty of Computer Science
  • 2.
  • 3.
  • 4. System presentation Initial data Minipar module Final result NE Rule Negation Rules Acronyms Background knowledge eXtended Wordnet GATE VerbOcean Numbers & Dates Contradiction Rules In RTE-3 competition Two-way 69.13 % Three-way 54.7 % After RTE-3 competition Test Data 2007: Two-way 74.1 % Three-way 71.8 % Test Data 2008 : Two-way 72.1 % Three-way 68.5 % Fitness Rule Main module TreeTagger Resources DIRT Dependency trees for (T, H) pairs LingPipe module Named entities for (T, H) pairs Wikipedia
  • 5. Example NE (N) subj loc adv (A) subj mod … … NE (N) loc … … DIRT, VerbOcean WordNet Acronyms, BK DIRT: solve=resolve 0.31453 DIRT: convict=arrest 0.28895 DIRT: convict=acquit 0.302455,OPPOSITE VerbOcean: increase<>decrease, VerbOcean: leave<>stay WordNet: trouble=problem WordNet: talk=discussion Acronym: EU=European Union BK: Buenos Aires [in] Argentine BK: 16 [is] sixteen Hypothesis Text verb H (V) noun (N) verb T (V) noun (N) … … … …
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18. Results in RTE4 68.50% 1000 685 1000 Total 54.25% 42.86% 73.89% 203 150 350 Unknown 58.72% 46.00% 81.18% 85 69 150 Contradiction 76.90% 93.20% 65.45% 712 466 500 Entailment F-measure Recall Precision Total offered by our system Correct offered by our system In Gold Answer Type 72.10% 1000 721 1000 Total 64.72% 51.00% 88.54% 288 255 500 No 76.90% 93.20% 65.45% 712 466 500 Yes F-measure Recall Precision Total offered by our system Correct offered by our system In Gold Answer Type
  • 19. Comparison with RTE-3 To be able to see each component’s relevance, the systems were run in turn with each component removed 5.55 % 68.10 % - - Without Contradiction rule 7.21 % 66.90 % 16.71 % 57.58 % Without NE rule 4.72 % 68.70 % 2.17 % 67.63 % Without Negation rule 2.36 % 70.40 % 2.00 % 67.75 % Without BK 0.42 % 71.80 % 1.08 % 68.38 % Without Acronyms 4.16 % 69.10 % 1.63 % 68.00 % Without WordNet 0.97 % 71.40 % 0.54 % 68.76 % Without DIRT Relevance Precision Relevance Precision RTE-4 RTE-3 System Description
  • 20.
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  • 32.