BIK Terminology

Solving the terminology puzzle, one posting at a time

Terminology extraction with memoQ 5.0 RC

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In the framework of a TermNet study, I have been researching and gathering data about terminology management systems (TMS). We will not focus on term extraction tools (TE), but since one of our tools candidates recently released a new term extraction module, I wanted to check it out. Here is what I learned from giving the TE functionality of memoQ 5.0 release candidate a good run.

Let me start by saying that this test made me realize again how much I enjoy working with terminological data; I love analyzing terms and concept, researching meaning and compiling data in entries; to me it is a very creative process. Note furthermore that I am not an expert in term extraction tools: I was a serious power-user of several proprietary term extraction tools at JDE and Microsoft; I haven’t worked with the Trados solution since 2003; and I have only played with a few other methods (e.g. Word/Excel and SynchroTerm). So, my view of the market at the moment is by no means a comprehensive one. It is, however, one of a user who has done some serious term mining work. One of the biggest projects I ever did was Axapta 4.0 specs. It took us several days to even just load all documents on a server directory; it took the engine at least a night to “spit out” 14,000 term candidates; and it took me an exhausting week to nail down 500 designators worth working with.

As a mere user, as opposed to a computational linguist, I am not primarily interested in the performance of the extraction engine (I actually think the topic is a bit overrated); I like that in memoQ I can set the minimum/maximum word lengths, the minimum frequency, and the inclusion/exclusion of words with numbers (the home-grown solutions had predefined settings for all of this). But beyond the rough selection, I can deal with either too many or too few suggestions, if the tool allows me to quickly add or delete what I deem the appropriate form. There will always be noise and lots of it. I would rather have the developer focus on the usability of the interface than “waste” time on tweaking algorithms a tiny bit more.

So, along the lines of the previous posting on UX design, my requirements on a TE tool are that it allows me to:

  • Process term candidates (go/no-go decision) extremely fast and
  • Move data into the TMS smoothly and flawlessly.

memoQ by Kilgray Translation Technologies* meets the first requirement very nicely. My (monolingual) test project was the PowerPoint presentations of the ECQA Certified Terminology Manager, which I had gone through in detail the previous week and which contained 28,979 English words. Because the subject matter is utterly familiar to me, there was no question as to what should make the cut and what shouldn’t. I loved that I could “race” through the list and go yay or nay; that I could merge obvious synonyms; and that I could modify term candidates to reflect their canonical form. Because the contexts for each candidate are all visible, I could have even checked the meaning in context quickly if I had needed to.

I also appreciated that there is already a stop word list in place. It was very easy to add to it, although here comes one suggestion: It would be great to have the term candidate automatically inserted in the stop-word dialog. Right now, I still have to type it in. It would safe time if it was prefilled. Since the stop word list is not very extensive (e.g. even words like “doesn’t” are missing in the English list), it’ll take everyone considerable time to build up a list, which in its core will not vary substantially from user to user. But that may be too much to ask for a first release.

As for my second requirement, memoQ term extraction doesn’t meet that (yet) (note that I only tested the transfer of data to memoQ, but not to qTerm). I know it is asking for a lot to have a workflow from cleaned-up term candidate list to terminological entry in a TMS. Here are two suggestions that would make a difference to users:

  • Provide a way to move context from the source document, incl. context source, into the new terminological entry.
  • Merging terms into one entry because they are synonyms is great. But they need to show up as synonyms when imported into the term base; none of my short forms (e.g. POS, TMS) showed up in the entry for the long forms (e.g. part of speech, terminology management systems) when I moved them into the memoQ term base.

My main overall wish is that we integrate TE with authoring and translation in a way that allows companies and LSPs, writers and translators to have an efficient workflow. It is imperative in technical communication/translation to document terms and concepts. When this task is put on the translators, it is already quite late, but it is better than if it doesn’t happen. Only fast and flawless processing will allow one-person or multi-person enterprises, for that matter, to carry out terminology work as part of the content supply chain. When the “fast and flawless” prerequisite is met, even those of my translator-friends who detest the term “content supply chain” will have enough time to enjoy themselves with the more creative aspects of their profession. Then, economic requirements essential on the macro level are met, and the need of the individual to get satisfaction out of the task is fulfilled on the micro level. The TE functionality of memoQ 5.0 RC excels in design and, in my opinion, is ready for translators’ use. If you have any comments, if you agree or disagree with me, I’d love to hear it.

*Kilgray is a client of BIK Terminology.

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Quantity AND Quality

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In If quantity matters, what about quality? I promised to shed some light on how to achieve quantity without skimping on quality. In knowledge management, it boils down to solid processes supported by reliable and appropriate tools and executed by skilled people. Let me drill down on some aspects of setting up processes and tools to support quantity and quality.

If you cannot afford to build up an encyclopedia for your company (and who can?), select metadata carefully. The number and types of data categories (DCs), as discussed in The Year of Standards, can make a big difference. That is not to say use less. Use the right ones for your environment.

Along those lines, hide data categories or values where they don’t make sense. For example, don’t display Grammatical Gender when Language=English; invariably a terminologist will accidentally select a gender, and if only a few users wonder why that is or note the error, but can’t find a way to alert you to it, too much time is wasted. Similarly, hide Grammatical Number, when the Part of Speech=Verb, and so on.

Plan dependent data, such as product and version, carefully. For example, if versions for all your products are numbered the same way (e.g. 1, 2, 3,..), it might be easiest to have two related tables. If most of your versions have very different version names, you could have one table that lists product and version together (e.g. Windows 95, Windows 2000, Windows XP, …); it makes information retrievable slightly simpler especially for non-expert users. Or maybe you cannot afford or don’t need to manage down to the version level because you are in a highly dynamic environment.

Enforce mandatory data when a terminologist releases (approves or fails) an entry. If you  decided that five out of your ten DCs are mandatory, let the tool help terminologists by not letting them get away with a shortcut or an oversight.

It is obviously not an easy task to anticipate what you need in your environment. But well-designed tools and processes support high quality AND quantity and therefore boost your return on investment.

On a personal note, Anton is exhausted with anticipation of our big upcoming event: He will be the ring bearer in our wedding this weekend.

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If quantity matters, what about quality?

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Linguistic quality is one of the persistent puzzles in our industry, as it is such an elusive concept. It doesn’t have to be, though. But if only quantity matters to you, you are on your way to ruining your company’s linguistic assets.

Because terminology management is not an end in itself, let’s start with the quality objective that users of a prescriptive terminology database are after. Most users access terminological data for support with monolingual, multilingual, manual or automated authoring processes. The outcomes of these processes are texts of some nature. The ultimate quality goal that terminology management supports with regard to these texts could be defined as “the text must contain correct terms used consistently.” In fact, Sue Ellen Wright “concludes that the terminology that makes up the text comprises that aspect of the text that poses the greatest risk for failure.” (Handbook of Terminology Management)

In order to get to this quality goal, other quality goals must precede it. For one, the database must contain correct terminological entries; and second, there must be integrity between the different entries, i.e. entries in the database must not contradict each other.

In order to attain these two goals, others must be met in their turn: The data values within the entries must contain correct information. And the entries must be complete, i.e. no mandatory data is missing. I call this the mandate to release only correct and complete entries (of course, a prescriptive database may contain pre-released entries that don’t meet these criteria yet).

Let’s see what that means for terminologists who are responsible for setting up, approving or releasing a correct and complete entry. They need to be able to:

  • Do research.
  • Transfer the result of the research into the data categories correctly.
  • Assure integrity between entries.
  • Approve only entries that have all the mandatory data.
  • Fill in an optional data category, when necessary.

Let’s leave aside for a moment that we are all human and that we will botch the occasional entry. Can you imagine if instead of doing the above, terminologists were told not to worry about quality? From now on, they would:

  • Stop at 50% research or don’t validate the data already present in the entry.
  • Fill in only some of the mandatory fields.
  • Choose the entry language randomly.
  • Add three or four different designations to the Term field.
  • ….

Do you think that we could meet our number 1 goal of correct and consistent terminology in texts? No. Instead a text in the source language would contain inconsistencies, spelling variations, and probably errors. Translations performed by translators would contain the same, possibly worse problems. Machine translations would be consistent, but they would consistently contain multiple target terms for one source term, etc. The translation memory would propagate issues to other texts within the same product, the next version of the product, to texts for other products, and so on. Some writers and translators would not use the terminology database anymore, which means that fewer errors are challenged and fixed. Others would argue that they must use the database; after all, it is prescriptive.

Unreliable entries are poison in the system. With a lax attitude towards quality, you can do more harm than good. Does that mean that you have to invest hours and hours in your entries? Absolutely not. We’ll get to some measures in a later posting. But if you can’t afford correct and complete entries, don’t waste your money on terminology management.

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