Wrong matches in Wordfast Thread poster: Elena Carbonell
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Elena Carbonell 
Netherlands
Local time: 00:29
Member (2007)
English to Spanish
+ ...
My problem is quite strange, I think.
I have been working many years with Wordfast (like 5 or so) and recently I am experiencing problems because I get TM matches which are completely wrong, I have set my Fuzzy Match Recognition very high to 85 and still I get strange results.
Here is an example of what happens:
I have a list of camera brand names.
In the list I get
Olympus
And the TM with a 85% of fuzzy match proposes the following
Sumini... See more My problem is quite strange, I think.
I have been working many years with Wordfast (like 5 or so) and recently I am experiencing problems because I get TM matches which are completely wrong, I have set my Fuzzy Match Recognition very high to 85 and still I get strange results.
Here is an example of what happens:
I have a list of camera brand names.
In the list I get
Olympus
And the TM with a 85% of fuzzy match proposes the following
Suministro eléctrico
When I check in the TM, that is a record of the following translation:
Power Supply
How do Olympus and Power Supply could be considered similar? I have found out that the matches have a few letters in common with the source language but that's just about it, shouldn't be enough to get a match even if it's fuzzy.
Any help on stopping these incredibly mad and terribly wrong matches?
And please ask me if I didn't explain me well, it's difficult to explain because it's just...well, weird. ▲ Collapse
| | | | esperantisto
Local time: 01:29
Member (2006)
English to Russian
+ ...
SITE LOCALIZER | Fuzzy matching is closer to alchemy than to exact science :-) | Aug 28, 2008 |
It works in a quite complicated way and does not work well on segments with just one or a couple of words. So, you'd rather build a good glossary.
| | | | Elena Carbonell
Netherlands
Local time: 00:29
Member (2007)
English to Spanish
+ ...
TOPIC STARTER | Thank you for your answer but... | Aug 28, 2008 |
Thank you for your answer but I have been working with Wordfast for years now and this is something that recently happened.
I have like 6 different glossaries divided in topics technical, general, marketing, law...
But I do translate lots of technical information and that always comes in tables where each cell is a segment (I don't segment individual words or a pair of words, Wordfast does it for me). I have always got the right fuzzy recognition, and fuzzy recognition as I know it ... See more Thank you for your answer but I have been working with Wordfast for years now and this is something that recently happened.
I have like 6 different glossaries divided in topics technical, general, marketing, law...
But I do translate lots of technical information and that always comes in tables where each cell is a segment (I don't segment individual words or a pair of words, Wordfast does it for me). I have always got the right fuzzy recognition, and fuzzy recognition as I know it should work at word level, not letter level which is what is happening now.
As I said I never have had encountered this problem before so there may be something else going on...
Any help , anyone? ▲ Collapse
| | | | Oliver Walter
United Kingdom
Local time: 23:29
German to English
+ ...
| Try the Yahoo group | Aug 28, 2008 |
Elena Carbonell wrote:
Any help , anyone?
I don't know the answer but I suggest you could try searching and/or asking in the Yahoo! group for Wordfast - it is very active.
On the www.yahoo.com page you should find "Groups" in the list on the left
Oliver
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Ken Cox
Local time: 00:29
German to English
+ ...
| my experience... | Aug 28, 2008 |
...is that Wordfast is sometimes very erratic in proposing fuzzy matches. I suppose it uses a more or less 'intelligent' algorithm to determine the match level, and it sometimes gets completely fooled. Çést la vie.
| | | | | strange results - Fuzzy Match Recognition | Aug 30, 2008 |
Elena Carbonell wrote:
Here is an example of what happens:
I have a list of camera brand names.
In the list I get
Olympus
And the TM with a 85% of fuzzy match proposes the following
Suministro eléctrico
When I check in the TM, that is a record of the following translation:
Power Supply
And please ask me if I didn't explain me well, it's difficult to explain because it's just...well, weird.
This occurs regularly when a glossary file originates from a Wfmemory file.
In the segments of the Wf memory.txt there are, for example, several words. When a glossary is made out of it, Wf makes random guesses. It is not a fault of Wf in this case.
One should never produce glossaries out of TM-s, unless the TM consists of just one word.
TM-s from glossaries? Yes, they can be useful.
Your case might be a different one, but it reminds me of the above tipical error.
D
| | | | Elena Carbonell
Netherlands
Local time: 00:29
Member (2007)
English to Spanish
+ ...
TOPIC STARTER | Problem "solved"in the yahoo thread | Sep 2, 2008 |
As Oliver proposed I posted the problem at the other forum.
Some other people had the same problem I had and in previous versions it has to do with how the program is done. It seems the fuzzy recognition uses another kind of algorithm for short segments.
Yves Champollion himself said that this is a known issue that has been solved in later versions of Wordfast.
I will just have to download the latest version and install it but I want to wait till version 6 comes out.
... See more As Oliver proposed I posted the problem at the other forum.
Some other people had the same problem I had and in previous versions it has to do with how the program is done. It seems the fuzzy recognition uses another kind of algorithm for short segments.
Yves Champollion himself said that this is a known issue that has been solved in later versions of Wordfast.
I will just have to download the latest version and install it but I want to wait till version 6 comes out.
And, Dragomir I always make my glossaries manually while I am translating, pressing on CTR+ALT+T, never out of TM, so probably this is another kind of problem than the one you are talking about.
Thank you all for your help and advice. ▲ Collapse
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