# Part-of-Speech Tagger for Bodo Language using Deep Learning approach

arXiv: https://arxiv.org/abs/2401.03175 · HTML: https://mlsanity.com/paper/2401.03175/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 6 Jan 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Visualizing and Understanding Recurrent Networks](https://mlsanity.com/paper/1506.02078/) (2015)
- [Character-Aware Neural Language Models](https://mlsanity.com/paper/1508.06615/) (2015)
- [BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages](https://mlsanity.com/paper/1710.02187/) (2017)
- [Straight to the Tree: Constituency Parsing with Neural Syntactic Distance](https://mlsanity.com/paper/1806.04168/) (2018)
- [A Multi-task Approach for Named Entity Recognition in Social Media Data](https://mlsanity.com/paper/1906.04135/) (2019)
- [Exploiting Linguistic Resources for Neural Machine Translation Using Multi-task Learning](https://mlsanity.com/paper/1708.00993/) (2017)
- [Attending to Characters in Neural Sequence Labeling Models](https://mlsanity.com/paper/1611.04361/) (2016)
- [Open Information Extraction from Question-Answer Pairs](https://mlsanity.com/paper/1903.00172/) (2019)
- [A Factoid Question Answering System for Vietnamese](https://mlsanity.com/paper/1803.00712/) (2018)
- [AsPOS: Assamese Part of Speech Tagger using Deep Learning Approach](https://mlsanity.com/paper/2212.07043/) (2022)

## Built on by (most cited first, 2 of 2)

- [Comparative Study of Zero-Shot Cross-Lingual Transfer for Bodo POS and NER Tagging Using Gemini 2.0 Flash Thinking Experimental Model](https://mlsanity.com/paper/2503.04405/) (2025)
- [NagaNLP: Bootstrapping NLP for Low-Resource Nagamese Creole with Human-in-the-Loop Synthetic Data](https://mlsanity.com/paper/2512.12537/) (2025)

## Abstract (arXiv)

Language Processing systems such as Part-of-speech tagging, Named entity recognition, Machine translation, Speech recognition, and Language modeling (LM) are well-studied in high-resource languages. Nevertheless, research on these systems for several low-resource languages, including Bodo, Mizo, Nagamese, and others, is either yet to commence or is in its nascent stages. Language model plays a vital role in the downstream tasks of modern NLP. Extensive studies are carried out on LMs for high-resource languages. Nevertheless, languages such as Bodo, Rabha, and Mising continue to lack coverage. In this study, we first present BodoBERT, a language model for the Bodo language. To the best of our knowledge, this work is the first such effort to develop a language model for Bodo. Secondly, we present an ensemble DL-based POS tagging model for Bodo. The POS tagging model is based on combinations of BiLSTM with CRF and stacked embedding of BodoBERT with BytePairEmbeddings. We cover several language models in the experiment to see how well they work in POS tagging tasks. The best-performing model achieves an F1 score of 0.8041. A comparative experiment was also conducted on Assamese POS taggers, considering that the language is spoken in the same region as Bodo.