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        <title>From Stigma to Support: A Parallel Monolingual Corpus and NLP Approach for Neutralizing Mental Illness Bias</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
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    <abstract>Negative attitudes and perceptions towards mental illness continue to be pervasive in our society. One of the factors contributing to and reinforcing this stigma is the usage of language that is biased against mental illness. Identifying biased language and replacing it with person-first, neutralized language is a first step towards eliminating harmful stereotypes and creating a supportive and inclusive environment for those living with mental illness. This paper presents a novel Natural Language Processing (NLP) system that aims to automatically identify biased text related to mental illness and suggest neutral language replacements without altering the original text’s meaning. Building on previous work in the field, this paper presents the Mental Illness Neutrality Corpus (MINC) comprising over 5500 mental illness-biased text and neutralized sentence pairs (in English), which is used to fine-tune a CONCURRENT model system developed by Pryzant et al. (2020). After evaluation, the model demonstrates high proficiency in neutralizing mental illness bias with an accuracy of 98.7%. This work contributes a valuable resource for reducing mental illness bias in text and has the potential for further research in tackling more complex nuances and multilingual biases.</abstract>
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%0 Conference Proceedings
%T From Stigma to Support: A Parallel Monolingual Corpus and NLP Approach for Neutralizing Mental Illness Bias
%A Choey, Mason
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F choey-2023-stigma
%X Negative attitudes and perceptions towards mental illness continue to be pervasive in our society. One of the factors contributing to and reinforcing this stigma is the usage of language that is biased against mental illness. Identifying biased language and replacing it with person-first, neutralized language is a first step towards eliminating harmful stereotypes and creating a supportive and inclusive environment for those living with mental illness. This paper presents a novel Natural Language Processing (NLP) system that aims to automatically identify biased text related to mental illness and suggest neutral language replacements without altering the original text’s meaning. Building on previous work in the field, this paper presents the Mental Illness Neutrality Corpus (MINC) comprising over 5500 mental illness-biased text and neutralized sentence pairs (in English), which is used to fine-tune a CONCURRENT model system developed by Pryzant et al. (2020). After evaluation, the model demonstrates high proficiency in neutralizing mental illness bias with an accuracy of 98.7%. This work contributes a valuable resource for reducing mental illness bias in text and has the potential for further research in tackling more complex nuances and multilingual biases.
%U https://aclanthology.org/2023.ranlp-1.28
%P 249-254

Markdown (Informal)

[From Stigma to Support: A Parallel Monolingual Corpus and NLP Approach for Neutralizing Mental Illness Bias](https://aclanthology.org/2023.ranlp-1.28) (Choey, RANLP 2023)

• From Stigma to Support: A Parallel Monolingual Corpus and NLP Approach for Neutralizing Mental Illness Bias (Choey, RANLP 2023)

ACL

• Mason Choey. 2023. From Stigma to Support: A Parallel Monolingual Corpus and NLP Approach for Neutralizing Mental Illness Bias. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 249–254, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.