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19. 11. 2015, Brno
Luděk Svozil
Impact of automated
translation on mining
knowledge from text data
Kapitola 1
strana 2
Introduction
• Statistical and hybrid machine
translation systems are gaining more
attention
• Apart from commercial services like
Google Translate and Bing, there are
number of projects aiming to bring the
benefits of big data knowledge to endusers
strana 3
EU projects on horizon
• Modern MT – aims to bring powerful,
ready to use MT system to desktop
users
http://www.modernmt.eu/
• LTI cloud – gathers language
technology components for easy use
in information systems
http://www.ltinnovate.org/lticloud
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• If machine translation is part of
preprocessing, would it benefit the
text-mining procces? And how?
• Earlier experiments have shown that
when combining scarce data across
different languages, MT provides great
simplification of problem
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Test data and experiment
• 20 000 reviews in 5 languages from
booking.com were subjected to
Google machine translation, stemming
and then c5.0 decision tree was
trained on them and evaluated using
cross-validation
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Results – % decrease in attributes
count
ES
FR
PL
CS
DE
translation
24%
17%
42%
40%
29%
stemming
37%
31%
20%
33%
16%
translation and
stemming
41%
35%
56%
53%
44%
strana 7
Results – avg. classification error
ES
FR
PL
CS
DE
Original
14,10%
14,10%
12,40%
Translated
14,10%
13,30%
11,30% 12,70% 12,00%
Stemmed
15,30%
14,00%
11,90% 11,80% 13,50%
Translated and stemmed
15,50%
15,50%
12,80%
14,60% 12,70%
13,70% 14,10%
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• To observe how well the translated
data would combine with native
English, another experiment was
made
• 10 000 English documents were
combined with another 10 000 from
different language, the other language
was then Google translated
strana 9
Results – avg. classification error
EN+FR
EN+PL
EN+DE
EN+ES
original
16,10%
14,80%
14,60%
17,30%
non-English language
translated
33,50%
33,90%
37,70%
36,10%
strana 10
Conclusions
• MT simplifies problem (reduces
dictionary) while doesn’t increase
classification error
• Attention must be paid, while
combining native and translated
documents
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• Další detaily, testy a porovnání rulebased a MT translátorů najdete v mé
bakalářské práci „Dolování znalostí
z vícejazyčných textových dat“, která
bude k dispozici během ledna-února
2016