A MACHINE LEARNING APPROACH FOR NAMED ENTITY RECOGNITION IN PERSIAN

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ASGARI BIDHENDI MAJID; MINAEI BIDGOLI BEHROUZ

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2019 | Issue:5

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Title

A MACHINE LEARNING APPROACH FOR NAMED ENTITY RECOGNITION IN PERSIAN

Author(s)

ASGARI BIDHENDI MAJID | MINAEI BIDGOLI BEHROUZ

Keywords

NAMED ENTITY RECOGNITION;NATURAL LANGUAGE PROCESSING

SOCIAL MEDIA CORPUS

PERSIAN LANGUAGE

Abstract

NAMED ENTITY RECOGNITION (NER) IS ONE OF THE MOST IMPORTANT PREREQUISITES FOR MANY NATURAL LANGUAGE PROCESSING TASKS. DESPITE THE MANY RECENT RESEARCH STUDIES ON THE PERSIAN NER, THE PROPOSED SOLUTIONS ARE NOT STILL AS ACCURATE AS ITS STATE OF THE ART IN ENGLISH. FURTHERMORE, TO THE BEST OF OUR KNOWLEDGE, ALL OF THE PUBLIC CORPORA FOR THE PERSIAN NER (INCLUDING PARSNER AND ARMANPERSONER) ARE BASED ON BIJANKHAN CORPUS, WHICH IS CREATED FROM THE CONTENT OF HAMSHAHRI NEWSPAPER IN 2004. CORRESPONDINGLY, ALL OF INDUSTRIAL NER MODELS IN THE PERSIAN, ARE SPECIAL-PURPOSE FOR THE NEWS DATA AND NOT FLEXIBLE ENOUGH TO BE RUN ON THE OTHER APPLICATIONS, SUCH AS SOCIAL MEDIA TEXTS. IN THIS PAPER, WE INTRODUCE PARSNER-SOCIAL, A CORPUS WHICH IS CRAFTED FROM THE SOCIAL MEDIA DATA AND A METHOD BASED ON CONDITIONAL RANDOM FIELDS FOR NAMED ENTITY RECOGNITION. THE METHOD IS COMPARED WITH THE STATE-OF-THE-ART NER METHODS IN THE PERSIAN LANGUAGE AND THE RESULTS PROVE THE SUPERIORITY OF THE PROPOSED METHOD TO THEM.

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APA: Copy

ASGARI BIDHENDI, MAJID, & MINAEI BIDGOLI, BEHROUZ. (2019). A MACHINE LEARNING APPROACH FOR NAMED ENTITY RECOGNITION IN PERSIAN. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949108/en

Vancouver: Copy

ASGARI BIDHENDI MAJID, MINAEI BIDGOLI BEHROUZ. A MACHINE LEARNING APPROACH FOR NAMED ENTITY RECOGNITION IN PERSIAN. 2019. Available from: https://sid.ir/paper/949108/en

IEEE: Copy

MAJID ASGARI BIDHENDI, and BEHROUZ MINAEI BIDGOLI, “A MACHINE LEARNING APPROACH FOR NAMED ENTITY RECOGNITION IN PERSIAN,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2019, [Online]. Available: https://sid.ir/paper/949108/en

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