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Reseach Article

Persian Named Entity Recognition based with Local Filters

by Morteza Kolali Khormuji, Mehrnoosh Bazrafkan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 4
Year of Publication: 2014
Authors: Morteza Kolali Khormuji, Mehrnoosh Bazrafkan

Morteza Kolali Khormuji, Mehrnoosh Bazrafkan . Persian Named Entity Recognition based with Local Filters. International Journal of Computer Applications. 100, 4 ( August 2014), 1-6. DOI=10.5120/17510-8062

@article{ 10.5120/17510-8062,
author = { Morteza Kolali Khormuji, Mehrnoosh Bazrafkan },
title = { Persian Named Entity Recognition based with Local Filters },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 4 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { },
doi = { 10.5120/17510-8062 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:29:03.489979+05:30
%A Morteza Kolali Khormuji
%A Mehrnoosh Bazrafkan
%T Persian Named Entity Recognition based with Local Filters
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 4
%P 1-6
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

Persian (Farsi) language named entity recognition is a challenging, difficult, yet important task in natural language processing. This paper presents an approach based on a Local Filters model to recognize Persian (Farsi) language named entities. It uses multiple dictionaries, which are freely available on the Web. A dictionary is a collection of phrases that describe named entities. The framework is composed of two stages: (1) detection of named entity candidates using dictionaries for lookups and (2) filtering of false positives based. Dictionary lookups are performed using an efficient prefix-tree data structure. Our dictionary ?? based recognizer performs on Persian (Farsi) language with up to 88. 95% precision, 79. 65% recall, and an 82. 73% F1 score using ASEM.

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Index Terms

Computer Science
Information Sciences


Persian Natural language processing Named Entity Recognition Local Filters