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    <titleInfo>
        <title>Bi-Quantum Long Short-Term Memory for Part-of-Speech Tagging</title>
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    <name type="personal">
        <namePart type="given">Pandey</namePart>
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        <namePart type="given">Pakray</namePart>
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            <title>Proceedings of the 20th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">D</namePart>
            <namePart type="given">Pawar</namePart>
            <namePart type="family">Jyoti</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Lalitha</namePart>
            <namePart type="given">Devi</namePart>
            <namePart type="family">Sobha</namePart>
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    <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.</abstract>
    <identifier type="citekey">shyambabu-partha-2023-bi</identifier>
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        <url>https://aclanthology.org/2023.icon-1.22</url>
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        <date>2023-12</date>
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            <start>301</start>
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%0 Conference Proceedings
%T Bi-Quantum Long Short-Term Memory for Part-of-Speech Tagging
%A Shyambabu, Pandey
%A Partha, Pakray
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F shyambabu-partha-2023-bi
%X 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.
%U https://aclanthology.org/2023.icon-1.22
%P 301-307

Markdown (Informal)

[Bi-Quantum Long Short-Term Memory for Part-of-Speech Tagging](https://aclanthology.org/2023.icon-1.22) (Shyambabu & Partha, ICON 2023)

• Bi-Quantum Long Short-Term Memory for Part-of-Speech Tagging (Shyambabu & Partha, ICON 2023)

ACL

• Pandey Shyambabu and Pakray Partha. 2023. Bi-Quantum Long Short-Term Memory for Part-of-Speech Tagging. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 301–307, Goa University, Goa, India. NLP Association of India (NLPAI).