Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Anand Kumar, A.M.Subject: search.filters.subject.Deep learning

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    Legal Text Analysis Using Pre-trained Transformers
    (Springer Science and Business Media Deutschland GmbH, 2022) Prajwal, M.P.; Anand Kumar, A.M.
    In this paper, we investigate the application of pre-trained transformers for text classification and similarity identification in the legal domain. We do several experiments applying various pre-trained transformer models to predict the descriptor of law or case based on text and identify similar cases. We consider an Indian Supreme Court judicial cases dataset containing cases and statutes and the EURLEX dataset containing approximately 57,000 documents and 4000 labels. EURLEX is a collection of treaties and laws related to the European Union. We preprocess the texts in the dataset and obtain embeddings from pre-trained transformers. Then, we use these embeddings as input to LSTM/BiLSTM layer to classify or predict similarity. Our results show that pre-trained transformers are sufficiently good when the length of the text to be classified or similarity predicted is small rather than large texts. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Sequential Memory Modelling for Video Captioning
    (Institute of Electrical and Electronics Engineers Inc., 2022) Puttaraja, P.; Nayaka, C.; Manikesh, M.; Sharma, N.; Anand Kumar, A.M.
    In recent years, the automatic generation of natural language descriptions of video has focused on deep learning research and natural voice processing. Video understanding has multiple applications such as video search and indexing, but video subtitles are a correct sophisticated topic for complex and diverse types of video content. However, the understanding between video and natural language sets remains an open issue to better understand the video and create multiple methods to create a set automatically. The deep learning method has a major focus on the direction of video processing with performance and high-speed computing capabilities. This polling discusses an encoder-decoder network end-in-frame based on a deep learning approach to generate caption. In this paper we will describe the model, dataset and parameters used to evaluate the model. © 2022 IEEE.