Named Entity Recognition (NER) is a sub-task of Natural Language Processing (NLP) that distinguishes entities from unorganized text into predefined categorization. In recent years, a lot of Bangla NLP subtasks have received quite a lot of attention; but Named Entity Recognition in Bangla still lags behind. In this research, we explored the existing state of research in Bangla Named Entity Recognition. We tried to figure out the limitations that current techniques and datasets face, and we would like to address these limitations in our research. Additionally, We developed a Gazetteer that has the ability to significantly boost the performance of NER. We also proposed a new NER solution by taking advantage of state-of-the-art NLP tools that outperform conventional techniques.

翻译：命名实体识别（NER）是自然语言处理（NLP）的一项子任务，旨在从非结构化文本中识别出预定义类别的实体。近年来，许多孟加拉语NLP子任务获得了广泛关注，但孟加拉语命名实体识别仍相对滞后。本研究系统梳理了孟加拉语命名实体识别的现有研究现状，深入分析了当前技术与数据集面临的局限性，并致力于在研究中解决这些问题。此外，我们构建了一个能够显著提升NER性能的地名词典，同时提出一种利用前沿NLP工具的新型NER解决方案，其性能优于传统方法。