Knax Shop Website Localization

May 14, 2015 admin 0 Comments


The first project I worked on was the KNAX Shop Website Localization. KNAX Shop is an online business held by a Dane Jacob Nielsen who sells innovative Danish-designed wall hooks:


The Team:


Jacob lives in France, and he had his website in 3 languages: Danish (the source language), English and French. The CMS he uses is called PrestaShop – this is a free eCommerce solution that now has 230,000 users.

I worked with John Di Rico, Josh Potter and Duncan Young.

In order to manage the project, we used a free cloud-based Project Management tool called Zoho Project.

My part included leveraging the old version of the website in order to create a base Translation Memory and glossaries.

Josh did the most challenging part of the project – he extracted the strings from the website in three languages: Danish, English and French.

Then we aligned the file pairs using the Wordfast Anywhere alignment tool and built an EN-DA TM as well as an EN-FR TM.

I was in charge of creating glossaries, and I did some research to find out what the best way is to extract terms from files automatically.

I first used, which is a free cloud-based solution powered by TAAS. But I was not happy with the results as it extracted very few bilingual terms. It is good in extracting monolingual terms, though.

I also tried SDL Multiterm Extract for the automatic term extraction from bilingual files. For the En-Fr pair, it only extracted 20 terms, which is too few, so I would not advise buying it.


Finally, AntConc is really good for monolingual term base extraction as it is a free solution that extracts a lot of terms (230 per 1000 words) and indicated frequency.

But I am a memoQ user, and I ended up using memoQ for leveraging monolingual files and matching the results.


Here is how the process looks:

1. Add a Stop Word List


MemoQ has a very limited number of Stopword lists – besides, these lists are too short. SDL Trados Studio provides long and very good SWL for a lot of languages.

However, adding them to memoQ can be a challenge as SWL in memoQ have their own syntax and extension (.mqres). Here is how you can do it:

– go to Resource Console – Stopword Lists

– export a SWL from memoQ (.mqres) to get mqres syntax (open in in Notepad++ or TextWrangler)

– add it to a Word document

– add an SDL SWL to the above Word file

– format it using wildcards to observe the .mqres syntax

– import it back to memoQ: give it a name, specify the language

  1. Extract two monolingual glossaries:


– go to Options – Extract Terms

– indicate the frequency and add a Stopword list

– generate candidates, accept them and export them to a memoQ termbase

– export the termbase as a csv file

3. Now that you have two columns (En-Fr, for example), you can compare and match them. First, convert the csv to Excel, then sort the left column alphabetically and start comparing and populating the right column.


4. Bonus

I also had to generate an En-Da glossary, but I do not speak Danish. So, I used Google Translate to do back translation.


Then I pasted the Danish translation done by Google Translate back to the Excel file as the third column and then started comparing – I now had a column in English and two columns in Danish (one was human-translated (leveraged from the website), the other was machine-translated, but 80% of Danish terms in those two columns were the same).

As a result, I generated 70 terms for En-Fr and En-Da language pairs, without speaking Danish!

When I attended the Google Translate workshop in Google, the GT speakers asked the audience: “What are the unusual ways you use Google Translate?” I shared this example, and the Google Translate speakers were apparently glad to hear it as they have never thought that GT could be used in this way.

Previous Post

Next Post