@inproceedings{r-etal-2025-teamvision,
title = "{T}eam{V}ision@{D}ravidian{L}ang{T}ech 2025: Detecting {AI} generated product reviews in {D}ravidian Languages",
author = "R, Shankari S and
P, Sarumathi and
B, Bharathi",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Rajiakodi, Saranya and
Palani, Balasubramanian and
Subramanian, Malliga and
Cn, Subalalitha and
Chinnappa, Dhivya",
booktitle = "Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = may,
year = "2025",
address = "Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.dravidianlangtech-1.110/",
doi = "10.18653/v1/2025.dravidianlangtech-1.110",
pages = "642--646",
ISBN = "979-8-89176-228-2",
abstract = "Recent advancements in natural language processing (NLP) have enabled artificial intelligence (AI) models to generate product reviewsthat are indistinguishable from those written by humans. To address these concerns, this study proposes an effective AI detector model capable of differentiating between AI-generated and human-written product reviews. Our methodology incorporates various machine learning techniques, including Naive Bayes, Random Forest, Logistic Regression, SVM, and deep learning approaches based on the BERT architecture.Our findings reveal that BERT outperforms other models in detecting AI-generated content in both Tamil product reviews and Malayalam product reviews."
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%0 Conference Proceedings
%T TeamVision@DravidianLangTech 2025: Detecting AI generated product reviews in Dravidian Languages
%A R, Shankari S.
%A P, Sarumathi
%A B, Bharathi
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Rajiakodi, Saranya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Cn, Subalalitha
%Y Chinnappa, Dhivya
%S Proceedings of the Fifth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2025
%8 May
%I Association for Computational Linguistics
%C Acoma, The Albuquerque Convention Center, Albuquerque, New Mexico
%@ 979-8-89176-228-2
%F r-etal-2025-teamvision
%X Recent advancements in natural language processing (NLP) have enabled artificial intelligence (AI) models to generate product reviewsthat are indistinguishable from those written by humans. To address these concerns, this study proposes an effective AI detector model capable of differentiating between AI-generated and human-written product reviews. Our methodology incorporates various machine learning techniques, including Naive Bayes, Random Forest, Logistic Regression, SVM, and deep learning approaches based on the BERT architecture.Our findings reveal that BERT outperforms other models in detecting AI-generated content in both Tamil product reviews and Malayalam product reviews.
%R 10.18653/v1/2025.dravidianlangtech-1.110
%U https://aclanthology.org/2025.dravidianlangtech-1.110/
%U https://doi.org/10.18653/v1/2025.dravidianlangtech-1.110
%P 642-646
Markdown (Informal)
[TeamVision@DravidianLangTech 2025: Detecting AI generated product reviews in Dravidian Languages](https://aclanthology.org/2025.dravidianlangtech-1.110/) (R et al., DravidianLangTech 2025)
ACL