Ruslan Kalitvianski

  • Citations Per Year
Learn More
This paper describes our participation in the shared task Named Entity Recognition in Twitter organized as part of the 2nd Workshop on Noisy User-generated Text. The shared task comprises two sub-tasks, concerning a) the detection of the boundaries of entities and b) the classification of the entities into one of 10 possible types. The proposed approach is(More)
This paper showcases three applications of GETALP's iMAG (Interactive Multilingual Access Gateway) technology. IMAGs allow internet users to navigate a selected website in the language of their choice (using machine translation), as well as to collaboratively and incrementally improve the translation through a web-based interface. One of GETALP's ongoing(More)
Access to textbooks in one’s own language, in parallel with the original version in the instructional language, is known to be quite helpful for foreign students studying abroad. Cooperative post-editing (PE) of specialized textbook pretranslations by the foreign students themselves is a good way to produce the ”target” versions, if the students find it(More)
This paper describes a corpus of nearly 10K French-Chinese aligned segments, produced by postediting machine translated computer science courseware. This corpus was built from 2013 to 2016 within the MACAU project, by native Chinese students. The quality, as judged by native speakers, is adequate for understanding (far better than by reading only the(More)
  • 1