Maximising Spanning Subtree Scores for Parsing Tree Approximations of Semantic Dependency Digraphs
Natalie Schluter · 2015
We present a method for finding the best tree approximation parse of a dependency digraph for a given sentence, with respect to a dataset of semantic digraphs as a computationally efficient and accurate alternative to DAG parsing.We present a training algorithm that learns the spanning subtree parses with the highest scores with respect to the data, and consider the output of this algorithm a description of the best tree approximations for digraphs of sentences from similar data.With the results from this approach, we acquire some important insights on the limits of solely data-driven tree approximation approaches to semantic dependency DAG parsing, and their rule-based, pre-processed tree approximation counterparts.