tekom-Jahrestagung 2012 - ActiveDoc

Lokalisierung und Übersetzung / Localization
LOC 17
Presentation
Automated monolingual term extraction
through spelling check: a case study
Uta Kreimeier, Olympus Soft Imaging Solutions GmbH, Münster
Olympus Soft Imaging Solutions GmbH is a global supply unit for microscope
image processing products within the Olympus group. It is
based in Münster, Germany. The software solutions are produced in
English and localized into several European and Asian languages.
Regarding software localization, the company faces the following challenges:
first, as the software solutions are used in science and research,
e.g. life sciences and material sciences, a high percentage of specialized
terminology from different areas has to be translated. In addition, each
software product is highly customizable and many dialogs are created at
run-time. As a result, many strings are difficult to translate because the
localization tool cannot display sufficient context information (e.g. dialog
preview). This leads to specialized terminology which is displayed
in string lists in the localization tool.
Second, the software is developed by an international and interdisciplinary
development team. This can lead to language problems in the
source language, which in turn can cause translation problems. The
situation could be mitigated by using an adapted source language. In
the localization tool, the English strings could be edited, and the resulting
adapted source language could then be translated. However, this
approach is not possible. Due to the number of people involved in each
project, every string change has to be reflected in the software, the specification
and the test plan. Furthermore, simship requires that software
localization starts early, and source strings are still being changed during
the localization stage. Consequently, language problems have to be
reported with a bug tracking tool, even if there is just a simple spelling
mistake.
This situation is very inconvenient for both, software development and
software localization. The specialized terminology without context leads
to translators’ questions and translation problems. Spelling mistakes are
often fixed at the end of the development phase and generate additional
late string changes.
A solution for the above challenges, the specialized terminology without
context information and the spelling mistakes, is an automated spelling
check with term extraction.
A prerequisite for this approach is a localization tool that offers a functionality
to mark whether a string is translatable or technical. The process
of marking strings is automated through rules such as keywords
that software developers supply in technical strings. These rules are the
base of a nightly string build process, which separates translatable and
technical strings and sends the results of a spelling check on translatable
strings to the software developers.
An automated spelling check is not acceptable unless it hardly returns
any false positives. It is therefore of paramount importance to create
and maintain a custom dictionary for the spelling check. The custom
dictionary is generated from the terminology database. This results in
a terminology database which contains entries that do not represent
tekom-Jahrestagung 2012
207