Riga: from FrameNet to Semantic Frames with C6.0 Rules

For the purposes of SemEval-2015 Task-18 on the semantic dependency parsing we combined the best-performing closed track approach from the SemEval-2014 competition with state-of-the-art techniques for FrameNet semantic parsing. In the closed track our system ranked third for the semantic graph accuracy and first for exact labeled match of complete semantic graphs. These results can be attributed to the high accuracy of the C6.0 rule-based sense labeler adapted from the FrameNet parser. To handle large SemEval training data the C6.0 algorithm was extended to provide multi-class classification and to use fast greedy search without significant accuracy loss compared to exhaustive search. A method for improved FrameNet parsing using semantic graphs is proposed.