Parts of speech tagging towards classical to quantum computing

Pandey, Shyambabu; Dadure, Pankaj; Nunsanga, Morrel V. L.

doi:10.1109/silcon55242.2022.10028796

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Book1

Article1

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

An Automatic POS Tagger System for Code Mixed Indian Social Media Text

Basisth,

Sachan,

Kumari

et al. 2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

An Automatic POS Tagger System for Code Mixed Indian Social Media Text

Basisth,

Sachan,

Kumari

et al. 2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

Part-of-speech tagger for Bodo language using deep learning approach

Pathak,

Narzary,

Nandi

et al. 2024

Nat. lang. processing

View full text Add to dashboard Cite

Language processing systems such as part-of-speech (POS) tagging, named entity recognition, machine translation, speech recognition, and language modeling have been 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 (LM) plays a vital role in the downstream tasks of modern natural language processing. Extensive studies are carried out on LMs for high-resource languages. However, these low-resource languages are still underreprese. In this study, we first present BodoBERT, an LM for the Bodo language. To the best of our knowledge, this work is the first such effort to develop an LM for Bodo. Second, we present an ensemble deep learning-based POS tagging model for Bodo. The POS tagging model is based on combinations of BiLSTM with conditional random field and stacked embedding of BodoBERT with BytePairEmbeddings. We cover several LMs 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.

show abstract