@inproceedings{shyambabu-partha-2023-bi,
    title = "Bi-Quantum Long Short-Term Memory for Part-of-Speech Tagging",
    author = "Pandey, Shyambabu  and
      Pakray, Partha",
    editor = "D. Pawar, Jyoti  and
      Lalitha Devi, Sobha",
    booktitle = "Proceedings of the 20th International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2023",
    address = "Goa University, Goa, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.icon-1.22/",
    pages = "301--307",
    abstract = "Natural language processing (NLP) is a subfield of artificial intelligence that enables computer systems to understand and generate human language. NLP tasks involved machine learning and deep learning methods for processing the data. Traditional applications utilize massive datasets and resources to perform NLP applications, which is challenging for classical systems. On the other hand, Quantum computing has emerged as a promising technology with the potential to address certain computational problems more efficiently than classical computing in specific domains. In recent years, researchers have started exploring the application of quantum computing techniques to NLP tasks. In this paper, we propose a quantum-based deep learning model, Bi-Quantum long short-term memory (BiQLSTM). We apply POS tagging using the proposed model on social media code-mixed datasets."
}