Persian Named Entity Recognition by Gray Wolf Optimization Algorithm

Abstract

Named entity recognition (NER) is a subfield of natural language processing (NLP). It is able to identify proper nouns, such as person names, locations, and organizations, and has been widely used in various tasks. NER can be practical in extracting information from social media data. However, the unstructured and noisy nature of social media (such as grammatical errors and typos) causes new challenges for NER, especially for low-resource languages such as Persian, and existing NER methods mainly focus on formal texts and English social media. To overcome this challenge, we consider Persian NER as an optimization problem and use the binary Gray Wolf Optimization (GWO) algorithm to segment posts into small possible phrases of named entities. Later, named entities are recognized based on their score. Also, we prove that even human opinion can differ in the NER task and compare our method with other systems with the $\mathrm{S}\mathrm{e}\mathrm{p}\_\mathrm{T}\mathrm{D}\_\mathrm{T}\mathrm{e}\mathrm{l}01$ dataset and the results show that our proposed system obtains a higher F1 score in comparison with other methods.