BibTeX
@MISC{Smith01detectionof,
author = {Noah A. Smith},
title = {Detection of Translational Equivalence},
year = {2001}
}
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Abstract
I propose a general algorithm for detecting translational equivalence between text samples in different languages. This algorithm is based on current approaches to duplicate detection, and it relies on information which can be automatically learned from parallel text. I also show experimental results which support the hypothesis that translational equivalence is empirically observable. In addition, these results suggest profitable directions for improving performance on this recognition task. 1 This work is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science with High Honors at the University of Maryland, College Park. Thesis advisor: Professor Philip S. Resnik, Departments of Linguistics and Computer Science and Institute for Advanced Computer Study. 1 The research presented here was supported in part by the National Science Foundation, Johns Hopkins University, and DARPA/ITO Cooperative Agreement N660010028910. Contents 1
Keyphrases
translational equivalence computer science profitable direction text sample college park advanced computer study recognition task high honor john hopkins university current approach general algorithm national science foundation partial fulfillment different language darpa ito cooperative agreement n660010028910 parallel text experimental result thesis advisor professor philip
