By I. Dan Melamed
Parallel texts (bitexts) are a goldmine of linguistic wisdom, as the translation of a textual content into one other language should be seen as a close annotation of what that textual content skill. wisdom approximately translational equivalence, which might be gleaned from bitexts, is of crucial value for functions corresponding to handbook and desktop translation, cross-language info retrieval, and corpus linguistics. the supply of bitexts has elevated dramatically because the creation of the internet, making their examine a thrilling new sector of analysis in normal language processing. This booklet lays out the speculation and the sensible strategies for locating and employing translational equivalence on the lexical point. it's a start-to-finish advisor to designing and comparing many translingual applications.
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Additional resources for Empirical Methods for Exploiting Parallel Texts
Less than half a word. 4 SIMR’s error distribution on the French/English “parliamentary debates” bitext. Errors were measured perpendicular to the main diagonal. 000 4 · 3 21 = 14 characters. 5 Another interesting comparison is in terms of maximum error. Certain applications of bitext maps, like the one described in chapter 4, can tolerate many small errors but no large ones. 4, SIMR’s bitext map was never off by more than 185 characters from any of the 7123 segment boundaries. 5 times the length of an average sentence (see chapter 4).
The algorithm sorts all chains on how many other chains they conflict with and eliminates them in this sort order, one at a time, until no conflicts remain. Whenever two or more chains are tied in the sort order, the conflict resolution algorithm eliminates all but the chain with the least point dispersal. Additional Search Passes To ensure that SIMR rejects spurious chains, the maximum angle deviation threshold must be set low. However, like any heuristic filter, this one will reject some perfectly valid candidates.
ADOMIT did not use this information; the algorithm has no notion of a line of text. However, a simple cross-check showed that ADOMIT found all of the omissions. The README file distributed with the bitexts admitted that the “human aligners weren’t infallible” and predicted “probably no more than five or so” alignment errors. ADOMIT corroborated this prediction by finding exactly five alignment errors. Thus, ADOMIT achieved perfect recall on both kinds of errors. 4 A Translator’s Tool As any translator knows, many omissions are intentional.
Empirical Methods for Exploiting Parallel Texts by I. Dan Melamed