Abstract
This paper describes the system of the team Orange-Deskiñ, used for the CoNLL 2017 UD Shared Task. We based our approach on an existing open source tool (BistParser), which we modified in order 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.
Cite
CITATION STYLE
Heinecke, J., & Asadullah, M. (2017). Multi-model and crosslingual dependency analysis. In CoNLL 2017 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (pp. 111–118). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/k17-3011
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