Dag Haug


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Expletives in Universal Dependency Treebanks
Gosse Bouma | Jan Hajic | Dag Haug | Joakim Nivre | Per Erik Solberg | Lilja Øvrelid
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

Although treebanks annotated according to the guidelines of Universal Dependencies (UD) now exist for many languages, the goal of annotating the same phenomena in a cross-linguistically consistent fashion is not always met. In this paper, we investigate one phenomenon where we believe such consistency is lacking, namely expletive elements. Such elements occupy a position that is structurally associated with a core argument (or sometimes an oblique dependent), yet are non-referential and semantically void. Many UD treebanks identify at least some elements as expletive, but the range of phenomena differs between treebanks, even for closely related languages, and sometimes even for different treebanks for the same language. In this paper, we present criteria for identifying expletives that are applicable across languages and compatible with the goals of UD, give an overview of expletives as found in current UD treebanks, and present recommendations for the annotation of expletives so that more consistent annotation can be achieved in future releases.


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Porting an Ancient Greek and Latin Treebank
John Lee | Dag Haug
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We have recently converted a dependency treebank, consisting of ancient Greek and Latin texts, from one annotation scheme to another that was independently designed. This paper makes two observations about this conversion process. First, we show that, despite significant surface differences between the two treebanks, a number of straightforward transformation rules yield a substantial level of compatibility between them, giving evidence for their sound design and high quality of annotation. Second, we analyze some linguistic annotations that require further disambiguation, proposing some simple yet effective machine learning methods.