When you’re relatively new to computer aided translation (CAT), the terminology can get a little confusing. In this post I want to describe the three main types of search used by translation memory managers (TMMs): memory search, glossary search, and concordance search.
Memory Search
This is the bread-and-butter feature of TMMs. Remember that a “translation memory” is basically just a database, where each entry is a string of source text, paired with its corresponding translation. The translation memory (TM) might have some other information, like who created each entry, how reliable the translation is, and so on, but the source-translation pair is the only essential part.
A memory search compares the sentence (or segment of text) that you’re currently translating against each source segment in your TM. If the TMM finds a match, then it displays the corresponding translation so you can insert it into your text without doing the translation again. This is the key function of a translation memory tool.
There are two kinds of matches that the TMM might find. The first is a perfect match: this is when there’s a source segment in the TM that’s identical to the sentence you’re currently translating. The other type of match is a fuzzy match: this is when there’s a source segment that’s similar but not identical to the sentence you’re translating. In either case, you need to decide whether to use the suggested translation (editing as needed), or ignore it and translate the sentence from scratch. Even with a perfect match, the translation might not fit in the current context; you’ve got to decide on a case-by-case basis. Which means that unfortunately, you can’t disconnect your brain when using translation memory.
Glossary Search
Another name for this is “terminology search.” Most TMMs these days have a glossary search function, although it might be an add-on with some. A glossary search matches source terms in a glossary against the sentence you’re translating; if it finds a matching term, the TMM suggests the term’s translation to you.
For example, if you’re translating the sentence “I like ice cream,” and your glossary has the entry [“ice cream” = “helado”], then your TMM will suggest the term “helado” to you.
It’s also possible to use fuzzy matching with glossary searches, although this isn’t as common a feature as with memory searches (Felix does have it).
Concordance Search
A concordance search (also called “context search”) is where you search your TM for occurrences of a particular term. They’re kind of the opposite of glossary searches: with a glossary search, the TMM is scanning the sentence you’re translating for matches in your glossary. With a concordance search, the TMM is scanning your translation memory for matches with the term you supply.
This feature can be really useful if you know you’ve translated a certain term before, but no matches are appearing in your memory search, and the term isn’t in your glossary. You can use a concordance search to find all the instances where you translated it before, the idea being that this should help you translate the term this time (and since you took the trouble to do a concordance search, maybe stick it into your glossary for the next time around).
For example, say you’re translating the sentence “I love ham for breakfast.” You don’t have any matches, but you know you’ve translated “ham” before (but can’t remember how). You could do a concordance search for “ham,” and might get matches like:
- We roast a ham every Christmas.
- That actor is a real ham.
(Which shows that it’s important to take the term’s context into account.) You could presumably then look at the translation for the first sentence, and see how you translated the edible type of ham in the past.
Those are the three main types of search performed by translation memory software. Now the next time you see these terms mentioned, you’ll know what they refer to. You can also see the Retrieval section of the Wikipedia article on translation memory.