<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hossain-etal-2023-nlp">
    <titleInfo>
        <title>NLP_CUET at BLP-2023 Task 1: Fine-grained Categorization of Violence Inciting Text using Transformer-based Approach</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jawad</namePart>
        <namePart type="family">Hossain</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hasan</namePart>
        <namePart type="given">Mesbaul</namePart>
        <namePart type="family">Ali Taher</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Avishek</namePart>
        <namePart type="family">Das</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mohammed</namePart>
        <namePart type="given">Moshiul</namePart>
        <namePart type="family">Hoque</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Firoj</namePart>
            <namePart type="family">Alam</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sudipta</namePart>
            <namePart type="family">Kar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Shammur</namePart>
            <namePart type="given">Absar</namePart>
            <namePart type="family">Chowdhury</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Farig</namePart>
            <namePart type="family">Sadeque</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ruhul</namePart>
            <namePart type="family">Amin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The amount of online textual content has increased significantly in recent years through social media posts, online chatting, web portals, and other digital platforms due to the significant increase in internet users and their unprompted access via digital devices. Unfortunately, the misappropriation of textual communication via the Internet has led to violence-inciting texts. Despite the availability of various forms of violence-inciting materials, text-based content is often used to carry out violent acts. Thus, developing a system to detect violence-inciting text has become vital. However, creating such a system in a low-resourced language like Bangla becomes challenging. Therefore, a shared task has been arranged to detect violence-inciting text in Bangla. This paper presents a hybrid approach (GAN+Bangla-ELECTRA) to classify violence-inciting text in Bangla into three classes: direct, passive, and non-violence. We investigated a variety of deep learning (CNN, BiLSTM, BiLSTM+Attention), machine learning (LR, DT, MNB, SVM, RF, SGD), transformers (BERT, ELECTRA), and GAN-based models to detect violence inciting text in Bangla. Evaluation results demonstrate that the GAN+Bangla-ELECTRA model gained the highest macro f₁-score (74.59), which obtained us a rank of 3rd position at the BLP-2023 Task 1.</abstract>
    <identifier type="citekey">hossain-etal-2023-nlp</identifier>
    <identifier type="doi">10.18653/v1/2023.banglalp-1.31</identifier>
    <location>
        <url>https://aclanthology.org/2023.banglalp-1.31</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>241</start>
            <end>246</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T NLP_CUET at BLP-2023 Task 1: Fine-grained Categorization of Violence Inciting Text using Transformer-based Approach
%A Hossain, Jawad
%A Ali Taher, Hasan Mesbaul
%A Das, Avishek
%A Hoque, Mohammed Moshiul
%Y Alam, Firoj
%Y Kar, Sudipta
%Y Chowdhury, Shammur Absar
%Y Sadeque, Farig
%Y Amin, Ruhul
%S Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F hossain-etal-2023-nlp
%X The amount of online textual content has increased significantly in recent years through social media posts, online chatting, web portals, and other digital platforms due to the significant increase in internet users and their unprompted access via digital devices. Unfortunately, the misappropriation of textual communication via the Internet has led to violence-inciting texts. Despite the availability of various forms of violence-inciting materials, text-based content is often used to carry out violent acts. Thus, developing a system to detect violence-inciting text has become vital. However, creating such a system in a low-resourced language like Bangla becomes challenging. Therefore, a shared task has been arranged to detect violence-inciting text in Bangla. This paper presents a hybrid approach (GAN+Bangla-ELECTRA) to classify violence-inciting text in Bangla into three classes: direct, passive, and non-violence. We investigated a variety of deep learning (CNN, BiLSTM, BiLSTM+Attention), machine learning (LR, DT, MNB, SVM, RF, SGD), transformers (BERT, ELECTRA), and GAN-based models to detect violence inciting text in Bangla. Evaluation results demonstrate that the GAN+Bangla-ELECTRA model gained the highest macro f₁-score (74.59), which obtained us a rank of 3rd position at the BLP-2023 Task 1.
%R 10.18653/v1/2023.banglalp-1.31
%U https://aclanthology.org/2023.banglalp-1.31
%U https://doi.org/10.18653/v1/2023.banglalp-1.31
%P 241-246

Markdown (Informal)

[NLP_CUET at BLP-2023 Task 1: Fine-grained Categorization of Violence Inciting Text using Transformer-based Approach](https://aclanthology.org/2023.banglalp-1.31) (Hossain et al., BanglaLP 2023)

• NLP_CUET at BLP-2023 Task 1: Fine-grained Categorization of Violence Inciting Text using Transformer-based Approach (Hossain et al., BanglaLP 2023)

ACL

• Jawad Hossain, Hasan Mesbaul Ali Taher, Avishek Das, and Mohammed Moshiul Hoque. 2023. NLP_CUET at BLP-2023 Task 1: Fine-grained Categorization of Violence Inciting Text using Transformer-based Approach. In Proceedings of the First Workshop on Bangla Language Processing (BLP-2023), pages 241–246, Singapore. Association for Computational Linguistics.