Above is essentially the process of collecting terms for reference.
There are 3 objectives of collecting translation terms.
Standardise terms during a project
Constitute an extensive reference base
Generate statistics of terms
Purpose of the translation database
Retain and standardise vocabulary on a project (like a termbase)
Provide search vocabulary for other projects
Provide synonyms and a deeper understanding of terms
Vocabulary, project tracking, wph, stats, link with accounts
Calculate translation volumes per month, customer and type of work
Store agency contacts, email addresses, logins to agency portals
Translation Database design
At the outset, I stored my translation terms in Evernote and on Google sheets. But after a while, searching for terms previously encountered was quite time-consuming.
I designed the translation database to store translation terms in source and target languages (French to English). It could be adapted for any language pair.
Terms are attached to a project but as they are in one table, I can easily search for any term from any project.
I realised that the database could also be used to generate statistics about projects and the domains in which I work. This is part of an ongoing attempt to define my profile as a technical translator.
The table structure
The essential tables are described below in a summary class diagram.
A term can be attached to only one project. A possible adaptation would be to allow terms to be attached to more than one project. Terms may have significantly different meanings depending on the context.
The Essential Fields
The technical translation process: Start a project, complete the number of words, the agency and end customer, the number of the estimate in accounts.
The agency table includes the contact details for the agency their payment conditions (30, 45 or 60 days), price in currency for translation, MTPE, Revision or Audiovisual.
So we have a project which links back to the agency and all terms are within a project.
The advantage of a bespoke database is to create functions tailor-made to your process. The essential requirement for me was to store terms and then be able to search for them from one location.
Search for terms
So while a project constrains terms just to a project, which helps to standardise terms with a project, terms are also available for search across all projects. So when working on a project, I often search for a term to see if I have come across it in previous projects and how I handled it. The following fields are searchable.
I calculate statistics of the number of terms in the database against the number of words translated in that domain
Calculate the number of terms by domain
The number of words per period to help me track whether I am in line with my objectives on the number of words.
Analysis of project domains
Where there are more words than terms, it means I just ‘get on with the project’ and don’t need so much support. Where there are more terms than words, it is generally because I went out to look for support and found a detailed online source.
See here for credits to some of my sources including Tech Dico, Le Grand Dictionnaire du Quebec, Linguee and specific data sources.
I have done a lot of words in computing (112k) but only have 1000 terms.
On the contrary, I collected many terms in ‘industrial’ because it was not my original speciality and a field that I wanted to focus on because of demand. Perhaps there are many more specific terms in engineering than in computing.
Scientific: these words came from a large project for Electrostatic discharge (ESD) specifications and guidelines for an electronic component manufacturer.
Legal and Business is more legal than business and may overlap with ‘management’ with many (24) smaller projects of around 1500 words.
There are some domains (Electrical Engineering and Nuclear) where I integrated a large terms’ database (42k) but have never specifically categorized a project in that domain.
There are some domains with large significant projects, 15k to 50k words
The largest project domains for me are ‘industrial’, ‘technical’ or ‘computing’.
I leave Electrical and Mechanical Engineering separate, because together would create a distinctively large category and would distort statistics.
Translation database statistics
I have set myself some objectives as part of managing my business, notably the number of words per month, average word rate and financial objectives based on the volume done.
Average word rate
Average translation rate: I measure the time taken for each project in Trello Plus and with the total number of words for each project calculate the number of words translated per hour. Access then calculates the average
Translation Database Terms stored
Two important fields: the chosen term in my target language (English) and the alternatives. This helps me reflect on the most appropriate term during translation.
At the end of a project, I may send a list of terms to the customer with the target files to show them the alternatives considered or for customers to choose the appropriate industry-specific term.
I store the abbreviations in French and English. It is often important to spell out an abbreviation to confirm its meaning and to determine, with customers, whether an abbreviation should be translated or left as is.
Deduplication of translation database terms
Terms may come up in more than one context or project. Sometimes the same word may mean different things in different contexts, or be combined with a strict duplicate.
Before building the database, I stored terms in Google Sheets, one for each project. So I had some work to do to combine these sheets together into the database and to help, built a deduplication function to combine terms.
This function is still useful to combine terms encountered on current projects. I take care though only to combine terms if they have the same meaning and are essentially synonyms, in which case the English term of one goes into the ‘alternative field’ of the other.
Part of my quality process is to check and standardise the terms used in my translation. Antidote helps me to do this because it highlights unknown and repeated words, providing a focus on the terms used.