Is there a way to fine tune settings to get better fuzzy matches? 投稿者: Brent Sørensen
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Let's say that I have the following segment in my TM.
Although this is still considered an acceptable alternative name, most botanists now use the name Lamiaceae in referring to this family.
My new translation has the following segment:
1. Most botanists now use the name Lamiaceae in referring to this family.
2. This is still considered an acceptable name, and most botanists now use it when referring to this family.
Both would give fuzzy matches with a... See more Let's say that I have the following segment in my TM.
Although this is still considered an acceptable alternative name, most botanists now use the name Lamiaceae in referring to this family.
My new translation has the following segment:
1. Most botanists now use the name Lamiaceae in referring to this family.
2. This is still considered an acceptable name, and most botanists now use it when referring to this family.
Both would give fuzzy matches with a score of 70% or so. However, Segment 1 gives a much more useful fuzzy match. All I have to do is delete the first part of the sentence. For Segment 2, I will essentially have to translate from scratch, and it will not save me much time.
Is there any way that I can adjust the settings (possibly penalty settings) so that I will get only fuzzy matches of Type 1. ▲ Collapse
| | | | | Better fuzzy match | Apr 17, 2020 |
You could write a regex to do a "cleanup" and convert to a single name.
| | | | Brent Sørensen 
ドイツ
Local time: 05:22
2016に入会
ドイツ語 から 英語
+ ...
TOPIC STARTER | Could you elaborate | Apr 17, 2020 |
Hi Anthony,
Thanks for your response. Could you elaborate on that a bit?
Thanks,
Brent
| | | | DZiW (X)
ウクライナ
英語 から ロシア語
+ ...
| A per-project fitted value | Apr 17, 2020 |
Brent, while certain language pairs combo may tend to specific fuzziness levels, CATs have different and proprietary formulae to calculate fuzzy matches (which is but Levenshtein/Edit distance modifications). To add to the confusion, texts in some fields and styles may be prone to fluctuation badly, so even with similar SVO-languages, I believe it’s a per-project value. Empirically... See more Brent, while certain language pairs combo may tend to specific fuzziness levels, CATs have different and proprietary formulae to calculate fuzzy matches (which is but Levenshtein/Edit distance modifications). To add to the confusion, texts in some fields and styles may be prone to fluctuation badly, so even with similar SVO-languages, I believe it’s a per-project value. Empirically increase the fuzzy match threshold, eliminating too many false positives.
On the other hand, I think it’s not funny when translators have to waste their time on tweaking and struggling with the hardware or software instead of doing the job. Why should one make wild guesses and wait for a prompt (fuzzy match) already knowing the translation, I wonder?--Unless it’s about mimicking somebody else’s ‘uniformity’ guidelines, lex, and style, of course. ▲ Collapse
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