SEMANTIC ROLE LABELING OF PERSIAN SENTENCES WITH MEMORY-BASED LEARNING APPROACH

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

KAMEL GHALIBAF A.; RAHATI GHOUCHANI S.; ESTAJI A.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: SIGNAL AND DATA PROCESSING Year:2009 | Volume:- | Issue:1 (SERIAL 11) Page(s): 13-22

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

1,842

Download:

Cites:

Information Journal Paper

Title

SEMANTIC ROLE LABELING OF PERSIAN SENTENCES WITH MEMORY-BASED LEARNING APPROACH

Author(s)

KAMEL GHALIBAF A. | RAHATI GHOUCHANI S. | ESTAJI A. | Issue Writer Certificate

Keywords

SEMANTIC ROLE LABELINGQ2

SHALLOW SEMANTIC PARSINGQ3

SHALLOW SYNTACTIC PARSINGQ3

MEMORY-BASED LEARNINGQ3

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. 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.

Cites

PLAGIARISM DETECTION IN THE SCIENTIFIC PAPERS USING SEMANTIC ROLE LABELING AND GENETIC ALGORITHM

References

No record.

Cite

APA: Copy

KAMEL GHALIBAF, A., RAHATI GHOUCHANI, S., & ESTAJI, A.. (2009). SEMANTIC ROLE LABELING OF PERSIAN SENTENCES WITH MEMORY-BASED LEARNING APPROACH. SIGNAL AND DATA PROCESSING, -(1 (SERIAL 11)), 13-22. SID. https://sid.ir/paper/160801/en

Vancouver: Copy

KAMEL GHALIBAF A., RAHATI GHOUCHANI S., ESTAJI A.. SEMANTIC ROLE LABELING OF PERSIAN SENTENCES WITH MEMORY-BASED LEARNING APPROACH. SIGNAL AND DATA PROCESSING[Internet]. 2009;-(1 (SERIAL 11)):13-22. Available from: https://sid.ir/paper/160801/en

IEEE: Copy

A. KAMEL GHALIBAF, S. RAHATI GHOUCHANI, and A. ESTAJI, “SEMANTIC ROLE LABELING OF PERSIAN SENTENCES WITH MEMORY-BASED LEARNING APPROACH,” SIGNAL AND DATA PROCESSING, vol. -, no. 1 (SERIAL 11), pp. 13–22, 2009, [Online]. Available: https://sid.ir/paper/160801/en

Related Journal Papers