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    <titleInfo>
        <title>Identifying Intent-Sentiment Co-reference from Legal Utterances</title>
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            <namePart type="given">Pawar</namePart>
            <namePart type="family">Jyoti</namePart>
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            <namePart type="given">Lalitha</namePart>
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    <abstract>Co-reference is always treated as one of challenging tasks under natural language processing and has been explored only in the domain of anaphora resolution to an extent. However, the benefit of it to identify the relations between multiple entities in a single context can be explored better while we aim to identify intent and sentiment from the utterances of a dialogue or conversation. The utilization of co-reference becomes more elegant while tracking users’ intents with respect to their corresponding sentiments explored in a specialized domain like judiciary. Thus, in the present attempt, we have identified not only intent and sentiment expressions at token level in an individual manner, we also classified the utterances and identified the co-reference between intent and sentiment entities in utterance level context. Last but not the least, the deep learning algorithms have shown improvements over traditional machine learning in all cases.</abstract>
    <identifier type="citekey">pinaki-dipankar-2023-identifying</identifier>
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%0 Conference Proceedings
%T Identifying Intent-Sentiment Co-reference from Legal Utterances
%A Pinaki, Karkun
%A Dipankar, Das
%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 pinaki-dipankar-2023-identifying
%X Co-reference is always treated as one of challenging tasks under natural language processing and has been explored only in the domain of anaphora resolution to an extent. However, the benefit of it to identify the relations between multiple entities in a single context can be explored better while we aim to identify intent and sentiment from the utterances of a dialogue or conversation. The utilization of co-reference becomes more elegant while tracking users’ intents with respect to their corresponding sentiments explored in a specialized domain like judiciary. Thus, in the present attempt, we have identified not only intent and sentiment expressions at token level in an individual manner, we also classified the utterances and identified the co-reference between intent and sentiment entities in utterance level context. Last but not the least, the deep learning algorithms have shown improvements over traditional machine learning in all cases.
%U https://aclanthology.org/2023.icon-1.7
%P 62-71

Markdown (Informal)

[Identifying Intent-Sentiment Co-reference from Legal Utterances](https://aclanthology.org/2023.icon-1.7) (Pinaki & Dipankar, ICON 2023)

• Identifying Intent-Sentiment Co-reference from Legal Utterances (Pinaki & Dipankar, ICON 2023)

ACL

• Karkun Pinaki and Das Dipankar. 2023. Identifying Intent-Sentiment Co-reference from Legal Utterances. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 62–71, Goa University, Goa, India. NLP Association of India (NLPAI).