Shallow Semantic Parsing of Persian Sentences

Extracting semantic roles is one of the major steps in representing text meaning. It refers to finding the semantic relations between a predicate and syntactic constituents in a sentence. In this paper we present a semantic role labeling system for Persian, using memory-based learning model and standard features. We show that good semantic parsing results can be achieved with a small 1300-sentence training set. In order to extract features, we developed a shallow syntactic parser which divides the sentence into segments with certain syntactic units. The input data for both systems is drawn from Hamshahri corpus which is hand-labeled with required syntactic and semantic information. The results show an F-score of 90.3% on argument boundary detection task and an F-score of 87.4% on semantic role labeling task using Gold-standard parses. An overall system performance shows an F-score of 83.8% on complete semantic role labeling system i.e. boundary plus classification.