Mining Rules for Rewriting States in a Transition-based Dependency Parser for English

Recently, methods for mining graph sequences have attracted considerable interest in datamining research. A graph sequence is a data structure used to represent changing networks. The aim of graph sequence mining is to enumerate common changing patterns appearing more frequently than a given threshold in graph sequences. Dependency analysis is recognized as a basic process in natural language processing. In transition-based parsers for dependency analysis, a transition sequence can be represented by a graph sequence, where each graph, vertex, and edge corresponds to a state, word, and dependency, respectively. In this paper, we propose a method for mining rules to rewrite states reaching incorrect final states to those reaching correct final states, from transition sequences of a dependency parser using a beam search. The proposed method is evaluated using an English corpus, and we demonstrate the design of effective feature templates based on knowledge obtained from the mined rules.