Resumen de Multi-Model and Crosslingual Dependency Analysis

Johannes Heinecke, Munshi Asadullah

• This paper describes the system of the team Orange-Deskin, used for the CoNLL 2017 UD Shared Task. We based our approach on an existing open source tool (BistParser), which we modified in or- der to produce the required output. Additionally we added a kind of pseudo-projectivisation. This was needed since some of the task’s languages have a high percentage of non-projective dependency trees. In most cases we also employed word embeddings. For the 4 surprise languages, the data provided seemed too little to train on. Thus we decided to use the training data of typologically close languages instead. Our system achieved a macro-averaged LAS of 68.61% (10th in the overall ranking) which improved to 69.38% after bug fixes