Semantic Role Labeling System Based on Dependency Tree Distance Method for Arguments Identification

In research on the semantic role labeling based on dependency,most systems apply machine learning to arguments identification and arguments classification.This paper analyses the characteristics of the dependency tree,and find that arguments distribute in specific area of dependency tree.Therefore,we propose a novel rule based method for the semantic role identification according to the dependency tree distance.The maximal distance from candidate arguments to verb is limited to no more than three.We also obtain best candidate arguments related to the verb.For the gold syntactic dependency tree,this method recognizes 98.5% of arguments on CoNLL 2009 Chinese dataset.Combined with arguments classification based on machine learning,the F measure of the system finally reaches 89.46%,which is a significant improvements compared with the previous work(81.68%).