The 23rd pacific asia on language, information and computation , 2009-12-04

Title : ( Shallow Semantic Parsing of Persian Sentences )

Authors: Azadeh Kamel Ghalibaf , Saeed Rahati , Azam Estaji ,

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Abstract

. 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

Keywords

, Semantic Role Labeling, Shallow Semantic Parsing, Shallow Syntactic Parsing, Memory-Based Learning.
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@inproceedings{paperid:1021432,
author = {Azadeh Kamel Ghalibaf and Saeed Rahati and Estaji, Azam},
title = {Shallow Semantic Parsing of Persian Sentences},
booktitle = {The 23rd pacific asia on language, information and computation},
year = {2009},
location = {هنگ کنگ},
keywords = {Semantic Role Labeling; Shallow Semantic Parsing; Shallow Syntactic Parsing; Memory-Based Learning.},
}

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%0 Conference Proceedings
%T Shallow Semantic Parsing of Persian Sentences
%A Azadeh Kamel Ghalibaf
%A Saeed Rahati
%A Estaji, Azam
%J The 23rd pacific asia on language, information and computation
%D 2009

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