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Showing 1 to 5 of 5 for “"scientific information extraction"”.

  1. Scalable information extraction with large language models

    Information extraction (IE) transforms unstructured text into structured knowledge such as entities and relations, and is fundamental to applications including knowledge graph construction, information retrieval, question answering, and domain-specific document understanding. Although large …

    temple Repository record for Scalable information extraction with large language models (opens in a new tab)

  2. Discovering Viral Hosts, Mutations, and Diseases using Machine Learning

    … studies available as unstructured text in scientific literature. We design an open-ended task for 'scientific information extraction (SIE)' from publications and propose a unique two-step retrieval augmented generation (RAG) framework for the same. We curate a novel dataset of mutations in …

    vt Repository record for Discovering Viral Hosts, Mutations, and Diseases using Machine Learning (opens in a new tab)

  3. Information Extraction from Scientific Literature

    The exponential growth of scientific literature, with millions of new articles published annually, has created an unsustainable discovery bottleneck across research communities. Manual extraction of critical information---including methodologies, datasets, and domain-specific terminologies---now …

    temple Repository record for Information Extraction from Scientific Literature (opens in a new tab)

  4. AI4Scientist: Accelerating and democratizing scientific research lifecycle

    Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms

    uiuc Repository record for AI4Scientist: Accelerating and democratizing scientific research lifecycle (opens in a new tab)

  5. Annotating and Automatically Extracting Task Descriptions from Shared Task Overview Papers in Natural Language Processing Domains

    The rapid growth rate of scientific literature makes it increasingly difficult for researchers to keep up with developments in their field. This is a problem that can be addressed by structuring academic papers according to information units that go deeper than keywords. The need to efficiently …

    umn Repository record for Annotating and Automatically Extracting Task Descriptions from Shared Task Overview Papers in Natural Language Processing Domains (opens in a new tab)