Conference Papers

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

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    Visual Question Answering Using Convolutional and Recurrent Neural Networks
    (Springer Science and Business Media Deutschland GmbH, 2023) Azade, A.; Saini, R.; Naik, D.
    This paper presents a methodology that deals with the task of generating answers corresponding to the respective questions which are based on the input images in the dataset. The model proposed in this methodology constitutes two major components and then integration of analysis results and features from these components to form a combination in order to predict the answers. We have created a pipeline that first preprocesses the dataset and then encodes the question string and answer string. Using NLP techniques like tokenization and stemming, text data is processed to form a vocabulary set. Yet another experiment with modification in model and approach was performed using easy-VQA dataset which is available publically. This model used the bag of words technique to turn a question into a vector. This approach considered two components separately for text and image feature extraction and merged it to form analysis and generate an answer. Merge is done by using element-wise multiplication. In these approaches, we have used the softmax activation function in the output layer to generate output or answer to the question. When compared to existing methodologies this approach seems comparable and gives decent results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Effective Information Retrieval, Question Answering and Abstractive Summarization on Large-Scale Biomedical Document Corpora
    (Springer Science and Business Media Deutschland GmbH, 2023) Shenoy, N.; Nayak, P.; Jain, S.; Kamath S․, S.; Sugumaran, V.
    During the COVID-19 pandemic, a concentrated effort was made to collate published literature on SARS-Cov-2 and other coronaviruses for the benefit of the medical community. One such initiative is the COVID-19 Open Research Dataset which contains over 400,000 published research articles. To expedite access to relevant information sources for health workers and researchers, it is vital to design effective information retrieval and information extraction systems. In this article, an IR approach leveraging transformer-based models to enable question-answering and abstractive summarization is presented. Various keyword-based and neural-network-based models are experimented with and incorporated to reduce the search space and determine relevant sentences from the vast corpus for ranked retrieval. For abstractive summarization, candidate sentences are determined using a combination of various standard scoring metrics. Finally, the summary and the user query are utilized for supporting question answering. The proposed model is evaluated based on standard metrics on the standard CovidQA dataset for both natural language and keyword queries. The proposed approach achieved promising performance for both query classes, while outperforming various unsupervised baselines. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.