Shilpa Chatterjee


2023

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VacLM at BLP-2023 Task 1: Leveraging BERT models for Violence detection in Bangla
Shilpa Chatterjee | P J Leo Evenss | Pramit Bhattacharyya
Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

This study introduces the system submitted to the BLP Shared Task 1: Violence Inciting Text Detection (VITD) by the VacLM team. In this work, we analyzed the impact of various transformer-based models for detecting violence in texts. BanglaBERT outperforms all the other competing models. We also observed that the transformer-based models are not adept at classifying Passive Violence and Direct Violence class but can better detect violence in texts, which was the task’s primary objective. On the shared task, we secured a rank of 12 with macro F1-score of 72.656%.

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LSJSP at SemEval-2023 Task 2: FTBC: A FastText based framework with pre-trained BERT for NER
Shilpa Chatterjee | Leo Evenss | Pramit Bhattacharyya | Joydeep Mondal
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

This study introduces the system submitted to the SemEval 2022 Task 2: MultiCoNER II (Multilingual Complex Named Entity Recognition) by the LSJSP team. We propose FTBC, a FastText-based framework with pre-trained Bert for NER tasks with complex entities and over a noisy dataset. Our system achieves an average of 58.27% F1 score (fine-grained) and 75.79% F1 score (coarse-grained) across all languages. FTBC outperforms the baseline BERT-CRF model on all 12 monolingual tracks.