Named Entity Recognition of Tunisian Arabic Using the Bi-LSTM-CRF Model

Abstract

Named Entity Recognition (NER) is an NLP field that deals with recognizing and classifying entities in written text. Most Arabic NER research studies discuss the Arabic NER challenge for the Modern Standard Arabic (MSA) language. However, the presence of dialectal Arabic textual resources in social media, blogs, TV shows, etc. is increasingly progressive. Therefore, the treatment of named entities is rapidly becoming a necessity, particularly for dialectal Arabic. In this paper, we are interested in the collection and annotation of a corpus as well as the realization of a NER system for Tunisian Arabic (TA), named TUNER. To the best of the researchers’ knowledge, this is the first study that uses the suggested method for this purpose. In the present study, we adopt a hybrid method based on a Bi-LSTM-CRF model and a rule-based method. The proposed TUNER system yields an F-measure of 91.43%. This is an interesting improvement over comparable related work dialectal Arabic NER systems.