DFKI-LT - Discriminant Ranking for Efficient Treebanking
Discriminant Ranking for Efficient Treebanking
2 Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), Coling 2010 Organizing Committee, 2010
Treebank annotation is a labor-intensive and time-consuming task. In this paper, we show that a simple statistical ranking model can signiﬁcantly improve treebanking efﬁciency by prompting human annotators, well-trained in disambiguation tasks for treebanking but not necessarily grammar experts, to the most relevant linguistic disambiguation decisions. Experiments were carried out to evaluate the impact of such techniques on annotation efﬁciency and quality. The detailed analysis of outputs from the ranking model shows strong correlation to the human annotator behavior. When integrated into the treebanking environment, the model brings a signiﬁcant annotation speed-up with improved inter-annotator agreement.
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