On the Feasibility of Character n-Grams Pseudo-Translation for Cross-Language Information Retrieval Tasks

Abstract

[Abstract] The field of Cross-Language Information Retrieval relates techniques close to both the Machine Translation and Information Retrieval fields, although in a context involving characteristics of its own. The present study looks to widen our knowledge about the effectiveness and applicability to that field of non-classical translation mechanisms that work at character $n$-gram level.} For the purpose of this study, an $n$-gram based system of this type has been developed. This system requires only a bilingual machine-readable dictionary of $n$-grams, automatically generated from parallel corpora, which serves to translate queries previously $n$-grammed in the source language. $n$-Gramming is then used as an approximate string matching technique to perform monolingual text retrieval on the set of $n$-grammed documents in the target language. The tests for this work have been performed on CLEF collections for seven European languages, taking English as the target language. The performance attained, close to the upper baseline, confirms the validity of character $n$-gram based approaches for Cross Language Information Retrieval tasks, both for indexing--retrieval and translation purposes, these not being tied to a given implementation.