A novel Data and Model Centric artificial intelligence based approach in developing high-performance Named Entity Recognition for Bengali Language.

Autor: Lima KA; Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh., Md Hasib K; Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh., Azam S; Faculty of Science and Technology, Charles Darwin University, Darwin, Northern Territory, Australia., Karim A; Faculty of Science and Technology, Charles Darwin University, Darwin, Northern Territory, Australia., Montaha S; Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh., Noori SRH; Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh., Jonkman M; Faculty of Science and Technology, Charles Darwin University, Darwin, Northern Territory, Australia.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Sep 22; Vol. 18 (9), pp. e0287818. Date of Electronic Publication: 2023 Sep 22 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0287818
Abstrakt: Named Entity Recognition (NER) plays a significant role in enhancing the performance of all types of domain specific applications in Natural Language Processing (NLP). According to the type of application, the goal of NER is to identify target entities based on the context of other existing entities in a sentence. Numerous architectures have demonstrated good performance for high-resource languages such as English and Chinese NER. However, currently existing NER models for Bengali could not achieve reliable accuracy due to morphological richness of Bengali and limited availability of resources. This work integrates both Data and Model Centric AI concepts to achieve a state-of-the-art performance. A unique dataset was created for this study demonstrating the impact of a good quality dataset on accuracy. We proposed a method for developing a high quality NER dataset for any language. We have used our dataset to evaluate the performance of various Deep Learning models. A hybrid model performed with the exact match F1 score of 87.50%, partial match F1 score of 92.31%, and micro F1 score of 98.32%. Our proposed model reduces the need for feature engineering and utilizes minimal resources.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Lima et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje