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pro vyhledávání: '"Chakravarthi, Bharathi Raja"'
This paper investigates the effectiveness of sentence-level transformers for zero-shot offensive span identification on a code-mixed Tamil dataset. More specifically, we evaluate rationale extraction methods of Local Interpretable Model Agnostic Expl
Externí odkaz:
http://arxiv.org/abs/2205.06119
Autor:
Ravikiran, Manikandan, Chakravarthi, Bharathi Raja, Madasamy, Anand Kumar, Sivanesan, Sangeetha, Rajalakshmi, Ratnavel, Thavareesan, Sajeetha, Ponnusamy, Rahul, Mahadevan, Shankar
Offensive content moderation is vital in social media platforms to support healthy online discussions. However, their prevalence in codemixed Dravidian languages is limited to classifying whole comments without identifying part of it contributing to
Externí odkaz:
http://arxiv.org/abs/2205.06118
Autor:
Karim, Md. Rezaul, Dey, Sumon Kanti, Islam, Tanhim, Shajalal, Md., Chakravarthi, Bharathi Raja
Publikováno v:
Pre-print for our paper at International Conference on Speech & Language Technology for Low-resource Languages (SPELLL'2022)
Numerous machine learning (ML) and deep learning (DL)-based approaches have been proposed to utilize textual data from social media for anti-social behavior analysis like cyberbullying, fake news detection, and identification of hate speech mainly fo
Externí odkaz:
http://arxiv.org/abs/2204.10196
Autor:
LekshmiAmmal, Hariharan RamakrishnaIyer, Ravikiran, Manikandan, Nisha, Gayathri, Balamuralidhar, Navyasree, Madhusoodanan, Adithya, Madasamy, Anand Kumar, Chakravarthi, Bharathi Raja
Hope Speech Detection, a task of recognizing positive expressions, has made significant strides recently. However, much of the current works focus on model development without considering the issue of inherent imbalance in the data. Our work revisits
Externí odkaz:
http://arxiv.org/abs/2204.05488
Autor:
Vasantharajan, Charangan, Benhur, Sean, Kumarasen, Prasanna Kumar, Ponnusamy, Rahul, Thangasamy, Sathiyaraj, Priyadharshini, Ruba, Durairaj, Thenmozhi, Sivanraju, Kanchana, Sampath, Anbukkarasi, Chakravarthi, Bharathi Raja, McCrae, John Phillip
Emotional Analysis from textual input has been considered both a challenging and interesting task in Natural Language Processing. However, due to the lack of datasets in low-resource languages (i.e. Tamil), it is difficult to conduct research of high
Externí odkaz:
http://arxiv.org/abs/2202.04725
Autor:
Benhur, Sean, Nayak, Roshan, Sivanraju, Kanchana, Hande, Adeep, Navaneethakrishnan, Subalalitha Chinnaudayar, Priyadharshini, Ruba, Chakravarthi, Bharathi Raja
Due to the exponentially increasing reach of social media, it is essential to focus on its negative aspects as it can potentially divide society and incite people into violence. In this paper, we present our system description of work on the shared t
Externí odkaz:
http://arxiv.org/abs/2112.15417
Autor:
Chakravarthi, Bharathi Raja, Priyadharshini, Ruba, Thavareesan, Sajeetha, Chinnappa, Dhivya, Thenmozhi, Durairaj, Sherly, Elizabeth, McCrae, John P., Hande, Adeep, Ponnusamy, Rahul, Banerjee, Shubhanker, Vasantharajan, Charangan
We present the results of the Dravidian-CodeMix shared task held at FIRE 2021, a track on sentiment analysis for Dravidian Languages in Code-Mixed Text. We describe the task, its organization, and the submitted systems. This shared task is the contin
Externí odkaz:
http://arxiv.org/abs/2111.09811
Autor:
Chakravarthi, Bharathi Raja, Chinnappa, Dhivya, Priyadharshini, Ruba, Madasamy, Anand Kumar, Sivanesan, Sangeetha, Navaneethakrishnan, Subalalitha Chinnaudayar, Thavareesan, Sajeetha, Vadivel, Dhanalakshmi, Ponnusamy, Rahul, Kumaresan, Prasanna Kumar
With the fast growth of mobile computing and Web technologies, offensive language has become more prevalent on social networking platforms. Since offensive language identification in local languages is essential to moderate the social media content,
Externí odkaz:
http://arxiv.org/abs/2111.03375
Autor:
Suryawanshi, Shardul, Chakravarthi, Bharathi Raja, Arcan, Mihael, Little, Suzanne, Buitelaar, Paul
Research into the classification of Image with Text (IWT) troll memes has recently become popular. Since the online community utilizes the refuge of memes to express themselves, there is an abundance of data in the form of memes. These memes have the
Externí odkaz:
http://arxiv.org/abs/2109.03571
Autor:
Chakravarthi, Bharathi Raja, Priyadharshini, Ruba, Ponnusamy, Rahul, Kumaresan, Prasanna Kumar, Sampath, Kayalvizhi, Thenmozhi, Durairaj, Thangasamy, Sathiyaraj, Nallathambi, Rajendran, McCrae, John Phillip
The increased proliferation of abusive content on social media platforms has a negative impact on online users. The dread, dislike, discomfort, or mistrust of lesbian, gay, transgender or bisexual persons is defined as homophobia/transphobia. Homopho
Externí odkaz:
http://arxiv.org/abs/2109.00227