Automatic Labeling of Semantic Roles in Persian Sentences using Dependency Trees

Automatic identification of words with semantic roles (such as Agent, Patient, Source, etc.) in sentences and attaching correct semantic roles to them, may lead to improvement in many natural language processing tasks including information extraction, question answering, text summarization and machine translation. Semantic role labeling systems usually take advantage of syntactic parsing and therefor the syntactic representation chosen affects the overall performance of the system. In this research, we present a semantic role labeling system based on full syntactic parsing. For this purpose, we use a dependency parser and machine learning methods. In our system, we have made an effort to overcome the problems of previous semantic role labelers for Persian, which all are based on shallow syntactic parsing. The outcome of the system is promising.