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A Distributed Architecture System for Recognizing Textual Entailment

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Faculty of Computer Science
Faculty of Computer Science

The document summarizes a distributed architecture system for recognizing textual entailment. The system uses a peer-to-peer network with caching mechanisms to improve speed. It transforms text and hypotheses into dependency trees using tools like LingPipe and MINIPAR. Resources like DIRT, WordNet, Wikipedia and an acronym database are used to calculate the fitness between text and hypothesis for determining entailment. The system achieved third place in the 2007 RTE competition with an accuracy of 69.13%.

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A Distributed Architecture System for Recognizing Textual Entailment Adrian Iftene, Alexandra Balahur-Dobrescu, Daniel Matei {adiftene, abalahur, dmatei}@info.uaic.ro „ 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],[object Object]
Textual Entailment ,[object Object],[object Object],[object Object],[object Object],[object Object]
RTE Competition ,[object Object],[object Object],[object Object],[object Object],[object Object]
System presentation Resources Initial data DIRT Minipar module Dependency trees for (T, H) pairs LingPipe module Named entities for (T, H) pairs Final result Core Module3 Core Module2 Core Module1 Acronyms Background knowledge Wordnet P2P Computers Wikipedia
Tools - LingPipe ,[object Object],[object Object],[object Object],[object Object],[object Object],Example: Hypothesis from pair 111: Leloir was born in Argentina. <ENAMEX TYPE=&quot;PERSON&quot;> Leloir </ENAMEX> was born in <ENAMEX TYPE=&quot;LOCATION&quot;> Argentina </ENAMEX>.
Tools - MINIPAR ,[object Object],Example: Le Beau Serge was directed by Chabrol . ( E0(() fin C * ) 1 (Le ~ U 3 lex-mod (gov Le Beau Serge)) 2 (Beau ~ U 3 lex-mod (gov Le Beau Serge)) 3 (Serge Le Beau Serge N 5 s (gov direct)) 4 (was be be 5 be (gov direct)) 5 (directed direct V E0 i (gov fin)) E2 (() Le Beau Serge N 5 obj (gov direct) (antecedent 3)) 6 (by ~ Prep 5 by-subj (gov direct)) 7 (Chabrol ~ N 6 pcomp-n (gov by)) 8 (. ~ U * punc) ) direct (V) Le_Beau_Serge (N) be (be) Chabrol (N) Le_Beau_Serge (N) Le (U) Beau (U) s be by obj lex-mod lex-mod
Resources – DIRT1 ,[object Object],Example: Le Beau Serge was directed by Chabrol &quot;X solves Y&quot; Y is solved by X X resolves Y X finds a solution to Y X tries to solve Y X deals with Y Y is resolved by X… N:s:V<direct>V:by:N N:obj:V<direct>V:by:N N:s:V<direct>V: :V<direct>V:by:N :V<direct>V:by:N N:obj:V<direct>V:
Resources – eXtended WordNet ,[object Object],[object Object]
Resources - Acronyms ,[object Object]
Background Knowledge - Example ,[object Object],[object Object],[object Object],[object Object]
Resources – Background Knowledge Argentine [is] Argentina ar |calling_code = 54 |footnotes = Argentina also has a territorial dispute Argentina', , Nación Argentina (Argentine Nation) for many legal purposes), is in the world. Argentina occupies a continental surface area of Argentina national football team Netherlands [is] Dutch Netherlands [is] Nederlandse Netherlands [is] Antillen Netherlands [in] Europe Netherlands [is] Holland Antilles [in] Netherlands “ Argentine”: Extracted Snippets from Wikipedia: ,[object Object],[object Object],[object Object],[object Object],Chinese [in] China Los Angeles [in] California 2 [is] two Netherlands [is] Holland
Semantic Variability Rules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Fitness calculation 1 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Text tree node mapping father mapping edge label mapping Hypothesis tree
Fitness calculation 2 ,[object Object],[object Object],[object Object]
Fitness calculation 3 ,[object Object],[object Object],[object Object],[object Object],[object Object],2.625 1 (SNCF, call, desc) 1.125 1 (company, call, obj) 3.048 0.096 (call, -, -) 4 1 (be, call, be) 2.5 1 (company, call, s) 3.125 1 (railway, company, nn) 3.125 1 (French, company, nn) 3.125 1 (the, company, det) Extended local fitness Node Fitness Initial entity
Results 0.6913 0.645 0.865 0.685 0.57 Run02 0.6913 0.635 0.87 0.69 0.57 Run01 Global SUM QA IR IE 0.6675 University of Rome ”Tor Vergata”, Italy 0.6687 LT-lab, Germany 0.6700 University of Texas, USA 0.6913 ” Al. I. Cuza” University, Romania 0.7225 LCC Richardson, USA 0.8000 Language Computer Corporation, USA
Peer-to-Peer Architecture ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Initiator DIRT db CM CM CM CM Acronyms SMB upload SMB download CM CM
Transfer protocol SMB header CIFS protocol
Synchronization problem ,[object Object]
Results 0:00:06.7 5 computers with 7 processes 4 0:00:41 One computer with full cache at start 3 2:03:13 One computer with caching mechanism, but with empty cache at start 2 5:28:45 One computer without caching mechanism 1 Duration Run details No
Conclusions ,[object Object],[object Object],[object Object],[object Object]
Acknowledgments ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]

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A Distributed Architecture System for Recognizing Textual Entailment

  • 1. A Distributed Architecture System for Recognizing Textual Entailment Adrian Iftene, Alexandra Balahur-Dobrescu, Daniel Matei {adiftene, abalahur, dmatei}@info.uaic.ro „ Al. I. Cuza“ University, Iasi, Romania Faculty of Computer Science
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  • 5. System presentation Resources Initial data DIRT Minipar module Dependency trees for (T, H) pairs LingPipe module Named entities for (T, H) pairs Final result Core Module3 Core Module2 Core Module1 Acronyms Background knowledge Wordnet P2P Computers Wikipedia
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  • 17. Results 0.6913 0.645 0.865 0.685 0.57 Run02 0.6913 0.635 0.87 0.69 0.57 Run01 Global SUM QA IR IE 0.6675 University of Rome ”Tor Vergata”, Italy 0.6687 LT-lab, Germany 0.6700 University of Texas, USA 0.6913 ” Al. I. Cuza” University, Romania 0.7225 LCC Richardson, USA 0.8000 Language Computer Corporation, USA
  • 18.
  • 19. Transfer protocol SMB header CIFS protocol
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  • 21. Results 0:00:06.7 5 computers with 7 processes 4 0:00:41 One computer with full cache at start 3 2:03:13 One computer with caching mechanism, but with empty cache at start 2 5:28:45 One computer without caching mechanism 1 Duration Run details No
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  • 24.