Editing Dictionary-based machine translation (section)

== "DKvec" ==
"DKvec is a method for extracting bilingual lexicons, from
noisy parallel corpora based on arrival distances of words in noisy parallel
corpora". This method has emerged in response to two problems plaguing the
statistical extraction of bilingual lexicons: "(1) How can noisy parallel
corpora be used? (2) How can non-parallel yet comparable corpora be used?"<ref name=":4">{{Cite book|title = Machine Translation and the Information Soup|volume = 1529|publisher = CR Subject Classification (1998): I.2.7, H.3, F.4.3, H.5, J.5 Springer-Verlag Berlin Heidelberg New York|isbn=978-3-540-65259-5|last = David Farwell Laurie Gerber Eduard Hovy|s2cid = 19677267|doi = 10.1007/3-540-49478-2|series = Lecture Notes in Computer Science|year = 1998|hdl = 11693/27676}}</ref>

The "DKvec" method has proven invaluable for machine
translation in general, due to the amazing success it has had in trials
conducted on both English – Japanese and English – Chinese noisy parallel
corpora. The figures for accuracy "show a 55.35% precision from a small corpus
and 89.93% precision from a larger corpus".<ref name=":4" /> With such impressive numbers it is
safe to assume the immense impact that methods such as "DKvec" has had in the
evolution of machine translation in general, especially Dictionary-Based
Machine Translation.

Algorithms used for extracting [[parallel corpora]] in a
bilingual format exploit the following rules in order to achieve a satisfactory
accuracy and overall quality:<ref name=":4" />
# Words have one sense per corpus
# Words have single translation per corpus
# No missing translations in the target document
# Frequencies of bilingual word occurrences are comparable
# Positions of bilingual word occurrences are comparable

This methods can be used to generate, or to look for, occurrence patterns which in turn are used to produce binary occurrence vectors which are used by the "DKvec" method.