The reason it is "very important" to me is that I do a lot of highly technical texts with very long segments. I get "dozens of term hits within one single sentence" all the time. The patent applications I do, for example, sometimes have extremely long sentences. Or sth like this, from a set of Excel files from a logistics project:

"dozenlift, goederenlift, beschrijving, verticaal transporteur, krattenlift, verticaal sorteren, sorter, rollenbaan, aangedreven rollen, rollerbaan, rollen, interroll, sew motor, patent, gepatenteerd, dozenheffer, krattenheffer, dozentransporteur, flexibel, integratie, eenvoudig, discontinue goederenlift, goederenlift, dozenlift, krattenlift, rollenbaan, aangedreven rollenbaan, interroll, roller drive, 24V trommelmotor, palletisers, palletiseermachines, flexibel, palletiseerrobot, palletizer, palletizers, palletiseermachine, palletiseersysteem, paletiser, palletiseren, automatisch stapelen" 

The above is a single segment. The results in the Term Recognition are completely impenetrable, visually. If I could make them alphabetical, and I was looking for, e.g., "dozentransporteur" (in bold above), I could then just visually scan to the "d" section and find it.

Of course they don't appear in alphabetical order within my sentence, but that's kind of irrelevant. When looking things up or searching for them in a large collection of words, humans are accustomed to searching in long lists alphabetically. It's deeply ingrained in our mind, and for very good reason: it's the fastest way to get from A to B  ;-)

Michael

Incidentally,  regarding segments like:

"dozenlift, goederenlift, beschrijving, verticaal transporteur, krattenlift, verticaal sorteren, sorter, rollenbaan, aangedreven rollen, rollerbaan, rollen, interroll, sew motor, patent, gepatenteerd, dozenheffer, krattenheffer, dozentransporteur, flexibel, integratie, eenvoudig, discontinue goederenlift, goederenlift, dozenlift, krattenlift, rollenbaan, aangedreven rollenbaan, interroll, roller drive, 24V trommelmotor, palletisers, palletiseermachines, flexibel, palletiseerrobot, palletizer, palletizers, palletiseermachine, palletiseersysteem, paletiser, palletiseren, automatisch stapelen"

I know that Studio doesn't offer auto-assembling (like memoQ, CafeTran and DVX2/3), but are there any tricks to auto-assemble such segments in Studio (maybe with an App?), using your termbases?

I have every one of the above terms in my client-specific termbase, so if I run into a new segment (with no fuzzy matches from TM), all of the CAT tools with an auto-assembling feature will be able to automatically translate such a segment. However, Studio can't do anything with it.

Michael 

Yes, sometimes I do segment texts like this slightly differently. For example, when translating patent applications, I tell the CAT tool to create a new segment at every semicolon, as this helps break things down into manageable chunks. However, this is not always possible with other kinds of texts, such as the one in this example.

I agree that shorter segments have a larger chance of producing automatic matches from my TM, but sometimes with texts like in my example, especially if it is a segment which is basically just a long list of separate terms, it is better to rely on auto-assembling from a client-specific TB, meaning there will be virtually no typing, as the CAT tool will do all the work for you.

Yes, AutoSuggest does show the correct term when I start typing the first letter in the target segment, but there is of course the old problem of not being able to type the first letter if you cannot remember the actual word, which actually happens to me quite a lot, especially with very long segments or when I'm tired. CafeTran has a handy solution for this as it has a system similar to Studio's Ctrl+Shift+L system, but where you are presented with an alphabetical drop-down list, where each letter of the alphabet can be expanded to show the terms starting with that particular letter. This is very cool, and borrowed from Wordfast Classic.

Michael

Indeed, I did think of this, but given the fragments are shown in context and the size of the segments Michael seems to work with I'm not sure how useful it would be for this usecase.

Indeed, I did think of this, but given the fragments are shown in context and the size of the segments Michael seems to work with I'm not sure how useful it would be for this usecase.