The growing amount of available information and the importance given to the access to technical information enhance the potential role of NLP applications in enabling users to deal with information for a variety of knowledge domains. In this process, language resources are crucial. This paper presents Lextec, a rich computational language resource for technical vocabulary in Portuguese. Encoding a representative set of terms for ten different technical domains, this concept-based relational language resource combines a wide range of linguistic information by integrating each entry in a domain-specific wordnet and associating it with a precise definition for each lexicalization in the technical domain at stake, illustrative texts and information for translation into English.

This paper reports the first results of an on-going research on evaluation of Machine Translation quality. The starting point for this work was the framework of ISLE (the International Standards for Language Engineering), which provides a classification for evaluation of Machine Translation. In order to make a quantitative evaluation of translation quality, we pursue a more consistent, fine-grained and comprehensive classification of possible translation errors and we propose metrics for sentence level errors, specifically lexical and syntactic errors.