# AsPOS: Assamese Part of Speech Tagger using Deep Learning Approach

arXiv: https://arxiv.org/abs/2212.07043 · HTML: https://mlsanity.com/paper/2212.07043/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 46 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## 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)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [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)
- [AsNER -- Annotated Dataset and Baseline for Assamese Named Entity recognition](https://mlsanity.com/paper/2207.03422/) (2022)
- [A Factoid Question Answering System for Vietnamese](https://mlsanity.com/paper/1803.00712/) (2018)

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

- [Performance Evaluation of Tokenizers in Large Language Models for the Assamese Language](https://mlsanity.com/paper/2410.03718/) (2024)
- [Part-of-Speech Tagger for Bodo Language using Deep Learning approach](https://mlsanity.com/paper/2401.03175/) (2024)

## Abstract (arXiv)

Part of Speech (POS) tagging is crucial to Natural Language Processing (NLP). It is a well-studied topic in several resource-rich languages. However, the development of computational linguistic resources is still in its infancy despite the existence of numerous languages that are historically and literary rich. Assamese, an Indian scheduled language, spoken by more than 25 million people, falls under this category. In this paper, we present a Deep Learning (DL)-based POS tagger for Assamese. The development process is divided into two stages. In the first phase, several pre-trained word embeddings are employed to train several tagging models. This allows us to evaluate the performance of the word embeddings in the POS tagging task. The top-performing model from the first phase is employed to annotate another set of new sentences. In the second phase, the model is trained further using the fresh dataset. Finally, we attain a tagging accuracy of 86.52% in F1 score. The model may serve as a baseline for further study on DL-based Assamese POS tagging.