Gazetteer-Enhanced Bangla Named Entity Recognition with BanglaBERT
  Semantic Embeddings K-Means-Infused CRF Model
Niloy Farhan Saman Sarker Joy Tafseer Binte Mannan Farig Sadeque
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.