Named Entity Recognition (NER) for Sino-Tibetan Languages: A Comprehensive Review and Status

Abstract

As technology continues to advance at a rapid pace, there is a growing interest in Natural Language Processing (NLP) tools and applications. However, creating NLP tools that can effectively process natural languages presents numerous difficulties. One crucial aspect of NLP is Named Entity Recognition (NER), which involves identifying and classifying named entities in a text based on their surrounding context. Although there has been extensive research, NER tagging still struggles to accurately tag unfamiliar named entities. NER for Sino-Tibetan languages, such as Bodo and Myanmar, poses various challenges, including word segmentation, lack of resources, and ambiguity. In this paper, we review the state-of-the-art in NER for Sino-Tibetan Languages, focusing on the methods, datasets, and performances achieved.  We also highlight underlying issues and future directions for NER research in this domain. Although there are not many works on NER related to Sino-Tibetan languages available, we tried to cover a good number of papers with a wide spectrum of languages, so that this review could be best utilised by researchers interested in NER studies and development for language technologies for languages from this group. As many as different works on Sino-Tibetan NER studies have been covered. We also tried to cover NER works with a variety of approaches and techniques ranging from rule-based to machine learning, deep learning, hybrid, and cross-lingual methods and highlighting their relevance towards the specific linguistic demands of Sino-Tibetan languages. Apart from these, we also reviewed a brief status on the NLP tasks for low-resourced languages, Bodo and Assamese. We have analyzed and presented in a structured way all the approaches, methods used, along with datasets, performances and challenges encountered. We hope that this paper can provide a comprehensive overview and a useful resource for the research community interested in NER for Sino-Tibetan languages.