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
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
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