Integrating Bilingual Named Entities Lexicon with Conditional Random Fields Model for Arabic Named Entities Recognition

Hkiri, Emna; Mallat, Souheyl; Zrigui, Mounir

doi:10.1109/icdar.2017.105

Cited by 6 publications

(1 citation statement)

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“…e obtained results were 86% of F-measure for the rule-based method, while LLR has outperformed the other statistical methods by obtaining 85% of precision. Another hybrid model for Arabic named entity recognition was proposed in [21]. e proposed method combines conditional random fields (CRFs), bilingual NE lexicon, and grammar rules to identify named entities.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] A New Rule‐Based Approach for Classical Arabic in Natural Language Processing

Salah

Mukred

Zakaria

et al. 2022

Journal of Mathematics

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Named entity recognition (NER) is fundamental in several natural language processing applications. It involves finding and categorizing text into predefined categories such as a person's name, location, and so on. One of the most famous approaches to identify named entity is the rule-based approach. This paper introduces a rule-based NER method that can be used to examine Classical Arabic documents. The proposed method relied on triggers words, patterns, gazetteers, rules, and blacklists generated by the linguistic information about entities named in Arabic. The method operates in three stages, operational stage, preprocessing stage, and processing the rule application stage. The proposed approach was evaluated, and the results indicate that this approach achieved a 90.2% rate of precision, an 89.3% level of recall, and an F-measure of 89.5%. This new approach was introduced to overcome the challenges related to coverage in rule-based NER systems, especially when dealing with Classical Arabic texts. It improved their performance and allowed for automated rule updates. The grammar rules, gazetteers, blacklist, patterns, and trigger words were all integrated into the rule-based system in this way.

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Section: Related Workmentioning

confidence: 99%