<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sazzed-2023-discourse">
    <titleInfo>
        <title>Discourse Mode Categorization of Bengali Social Media Health Text</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Salim</namePart>
        <namePart type="family">Sazzed</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, &amp; Social Media Analysis</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Jeremy</namePart>
            <namePart type="family">Barnes</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Orphée</namePart>
            <namePart type="family">De Clercq</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Roman</namePart>
            <namePart type="family">Klinger</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Toronto, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The scarcity of annotated data is a major impediment to natural language processing (NLP) research in Bengali, a language that is considered low-resource. In particular, the health and medical domains suffer from a severe paucity of annotated data. Thus, this study aims to introduce BanglaSocialHealth, an annotated social media health corpus that provides sentence-level annotations of four distinct types of expression modes, namely narrative (NAR), informative (INF), suggestive (SUG), and inquiring (INQ) modes in Bengali. We provide details regarding the annotation procedures and report various statistics, such as the median and mean length of words in different sentence modes. Additionally, we apply classical machine learning (CML) classifiers and transformer-based language models to classify sentence modes. We find that most of the statistical properties are similar in different types of sentence modes. To determine the sentence mode, the transformer-based M-BERT model provides slightly better efficacy than the CML classifiers. Our developed corpus and analysis represent a much-needed contribution to Bengali NLP research in medical and health domains and have the potential to facilitate a range of downstream tasks, including question-answering, misinformation detection, and information retrieval.</abstract>
    <identifier type="citekey">sazzed-2023-discourse</identifier>
    <identifier type="doi">10.18653/v1/2023.wassa-1.6</identifier>
    <location>
        <url>https://aclanthology.org/2023.wassa-1.6</url>
    </location>
    <part>
        <date>2023-07</date>
        <extent unit="page">
            <start>52</start>
            <end>57</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Discourse Mode Categorization of Bengali Social Media Health Text
%A Sazzed, Salim
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sazzed-2023-discourse
%X The scarcity of annotated data is a major impediment to natural language processing (NLP) research in Bengali, a language that is considered low-resource. In particular, the health and medical domains suffer from a severe paucity of annotated data. Thus, this study aims to introduce BanglaSocialHealth, an annotated social media health corpus that provides sentence-level annotations of four distinct types of expression modes, namely narrative (NAR), informative (INF), suggestive (SUG), and inquiring (INQ) modes in Bengali. We provide details regarding the annotation procedures and report various statistics, such as the median and mean length of words in different sentence modes. Additionally, we apply classical machine learning (CML) classifiers and transformer-based language models to classify sentence modes. We find that most of the statistical properties are similar in different types of sentence modes. To determine the sentence mode, the transformer-based M-BERT model provides slightly better efficacy than the CML classifiers. Our developed corpus and analysis represent a much-needed contribution to Bengali NLP research in medical and health domains and have the potential to facilitate a range of downstream tasks, including question-answering, misinformation detection, and information retrieval.
%R 10.18653/v1/2023.wassa-1.6
%U https://aclanthology.org/2023.wassa-1.6
%U https://doi.org/10.18653/v1/2023.wassa-1.6
%P 52-57

Markdown (Informal)

[Discourse Mode Categorization of Bengali Social Media Health Text](https://aclanthology.org/2023.wassa-1.6) (Sazzed, WASSA 2023)

• Discourse Mode Categorization of Bengali Social Media Health Text (Sazzed, WASSA 2023)

ACL

• Salim Sazzed. 2023. Discourse Mode Categorization of Bengali Social Media Health Text. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 52–57, Toronto, Canada. Association for Computational Linguistics.