A Token Classification Approach to Dependency Parsing

The Dependency-based syntactic parsing task consists in identifying a head word for each word in an input sentence. Hence, its output is a rooted tree where the nodes are the words in the sentence. State-of-the-art dependency parsing systems use transition-based or graph-based models. We present a token classification approach to dependency parsing, where any classification algorithm can be used. To evaluate its effectiveness, we apply the Entropy Guided Transformation Learning algorithm to the CoNLL 2006 corpus, using the Unlabelled Attachment Score as the accuracy metric. Our results show that the generated models are close to the average CoNLL system performance. Additionally, these findings also indicate that the token classification approach is a promising one.