برچسب زني نقش معنايي جملات فارسي با رويكرد يادگيري مبتني بر حافظه

Semantic Role Labeling of Persian Sentences with Memory-Based Learning Approach

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. Our proposed system implements a two-phase architecture to first identify the arguments by a shallow syntactic parser or chunker, and then to label them with appropriate semantic role, with respect to the predicate of the sentence. 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 RCISP1 corpus which is hand-labeled with required syntactic and semantic information. The results show an F-score of 81.6% 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 73.8% on complete semantic role labeling system i.e. boundary plus classification.