Memory-Based Dependency Parsing

Abstract

Abstract. This paper describes experiments on using inductive machine learning to guide a deterinistic dependency parser for unrestricted natural language text. Using data from a small treebank of Swedish, an eager probabilistic learning algorithm is used to induce context-sensitive parse tables. Evaluation shows a significant improvement over the baseline, which uses a table without contextual information. 1