Conference Papers

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

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Subject: search.filters.subject.Transformer models

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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.
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    Transformer and Knowledge Based Siamese Models for Medical Document Retrieval
    (Institute of Electrical and Electronics Engineers Inc., 2023) Dash, A.; Merchant, A.M.; Chintawar, S.; Kamath S․, S.
    Vocabulary mismatch is a significant issue when it comes to query-based document retrieval in the medical field. Since the documents are typically authored by professionals, they may contain many specialized terms that are not widely understood or used. Traditional information retrieval (IR) models like vector space and best match-based models fail in this regard. Neural Learning to Rank (NLtR) and transformer models have attracted significant research attention in the field of IR. Recent works in the medical field utilize medical knowledge bases (KB) that map words to concepts and aid in connecting several words to the same concept. In this paper, we present various Siamese-structured transformer and knowledge-based retrieval models designed to address the retrieval issues in the medical domain. The experimental evaluation highlighted the superior performance of the proposed retrieval model, and the best one, based on the UMLSBert ENG transformer, achieved best-in-class performance with respect to all evaluation metrics. © 2023 IEEE.