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A Distributed Architecture System for Recognizing Textual Entailment

Authors: Adrian Iftene; Alexandra Balahur-Dobrescu; Daniel Matei;

A Distributed Architecture System for Recognizing Textual Entailment

Abstract

Solving complex problems has become a usual fact in the natural language processing domain, where it is normal to use large information databases like lexicons, semantic relations, dictionaries. This paper describes the steps followed in building the system participating in the RTE3 competition. The system architecture is based on the peer-to-peer model, using the SMB protocol for file transfer. The system functionality was optimized by using a caching mechanism and a quota for synchronizing the termination of all processes. After the peer-to-peer network is configured, one computer becomes the initiator and builds the list of available neighbors. Subsequently, the initiator has the following additional roles: split the initial problem into sub-problems, send the sub-problems to the list of neighbors for solving, receive the partial output files and build the final solution.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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