Resolving Ambiguity in Inter-chunk Dependency Parsing

Recently, dependency grammar has become quite popular in relatively free word-order languages. We encounter many structural ambiguities when parsing a sentence using dependency grammar. We use a chunking procedure to avoid constructing a mistaken dependency structure. Chunking reduces the scope of dependency relations between dependents and governors. This paper presents a method to resolve ambiguity in inter-chunk dependency parsing by using valency information, a structural preference rule and a statistical model. The proposed method is a combination of a rule-based approach and a statistical model. The structural preference rule is an important clue to resolve ambiguity and complements the valency information. The statistical method, using structural, semantic, and lexical information, is applied to resolve ambiguity when selecting the governor of adjuncts. Experimental results show that dependency parsing using this method resolves ambiguity in inter-chunk dependency parsing with 88.03 % accuracy.