While there is a massive adoption of MT post-editing as a new service in the global translation industry, a common reference to skills and best practices to do this work well has been missing. TAUS took up the challenge to provide a course that would integrate with the DQF tools and the post-editing best practices developed by TAUS members in the previous years and offers both theory and practice to develop post-editing skills. The contribution of language service providers who are involved in MT and post-editing on a daily basis allowed TAUS to deliver fast on this industry need. This online course addresses the challenges for linguists and translators deciding to work on post-editing assignments and is aimed at those who want to learn the best practices and skills to become more efficient and proficient in the activity of post-editing.

While there is a massive adoption of MT post-editing as a new service in the global translation industry, a common reference to skills and best practices to do this work well has been missing. TAUS took up the challenge to provide a course that would integrate with the DQF tools and the post-editing best practices developed by TAUS members in the previous years and offers both theory and practice to develop post-editing skills. The contribution of language service providers who are involved in MT and post-editing on a daily basis allowed TAUS to deliver fast on this industry need. This online course addresses the challenges for linguists and translators deciding to work on post-editing assignments and is aimed at those who want to learn the best practices and skills to become more efficient and proficient in the activity of post-editing.

Word Sense Disambiguation (WSD) systems require large sense-tagged corpora along with lexical databases to reach satisfactory results. The number of English language resources for developed WSD increased in the past years while most other languages are still under-resourced. The situation is no different for Dutch. In order to overcome this data bottleneck, the DutchSemCor project will deliver a Dutch corpus that is sense-tagged with senses from the Cornetto lexical database. In this paper, we discuss the different conflicting requirements for a sense-tagged corpus and our strategies to fulfill them. We report on a first series of experiments to sup- port our semi-automatic approach to build the corpus.

The goal of this paper is to describe the annotation protocols and the Semantic Annotation Tool (SAT) used in the DutchSemCor project. The DutchSemCor project is aiming at aligning the Cornetto lexical database with the Dutch language corpus SoNaR. 250K corpus occurrences of the 3,000 most frequent and most ambiguous Dutch nouns, adjectives and verbs are being annotated manually using the SAT. This data is then used for bootstrapping 750K extra occurrences which in turn will be checked manually. Our main focus in this paper is the methodology applied in the project to attain the envisaged Inter-annotator Agreement (IA) of =80%. We will also discuss one of the main objectives of DutchSemCor i.e. to provide semantically annotated language data with high scores for quantity, quality and diversity. Sample data with high scores for these three features can yield better results for co-training WSD systems. Finally, we will take a brief look at our annotation tool.