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ShareScore release 0.9.0
Dataset results
4 results for “document retrieval”
Models and Data for Simple Applications of BERT for Ad Hoc Document Retrieval
<p>This submission includes all pretrained models, test data and prediction files for the arXiv paper "<a href="https://arxiv.org/abs/1903.10972">Simple Applications of BERT for Ad Hoc Document Retrieval</a>". Please follow the instructions at the <a href="https://github.com/castorini/birch">Birch repo</a> to reproduce the results.</p>
Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval
<p>This submission includes all pretrained models, test data and prediction files for the EMNLP 2019 paper "Cross-Domain Modeling of Sentence-Level Evidence for Document Retrieval". Please follow the instructions in the emnlp bran at the <a href="https://github.com/castorini/birch/tree/emnlp">Birch repo</a> to reproduce the results.</p>
musiXplora: Documentation - Data Retrieval
<h1>musiXplora: Structured Data Access</h1> <p>This documentation provides an overview of structured musiXplora data, that are now persistently accessible on Zenodo utilizing its DOI system. MusiXplora is a linked knowledge base for musicological and organological data developed and maintained by the <strong>Research Center <em>DIGITAL ORGANOLOGY</em></strong> at <strong>Leipzig University</strong>.</p> <p>Currently, the German versions are available only, but it is planned to extend this data after suitable translations were found.</p> <p>Code snippets will are provided for Python, JavaScript, and Bash (cURL), in order to assist with accessing the latest available data and retrieve the desired information efficiently.</p> <p>Rate Limits for Zenodo are listed <a href="https://developers.zenodo.org/#rate-limiting">here</a>.</p> <p>Available musiXplora-IDs with the corresponding latest DOIs are listed here: <a href="https://doi.org/10.5281/zenodo.11581620">musiXplora-Zenodo-Dictionary</a>.</p> <p>For further questions or requests, please refer to: redaktion@musixplora.de</p> <p>Version of Documentation: 0.0.1 (11 June, 2024)</p>
ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents [HisIR19] Dataset
<p>This dataset contains the training and test set used in the ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents.</p> <p>This competition investigates the performance of large-scale retrieval of historical document images based on<br> writing style. Based on large image data sets provided by cultural heritage institutions and digital libraries, providing<br> a total of 20 000 document images representing about 10 000 writers, divided in three types: writers of (i) manuscript books, (ii) letters, (iii) charters and legal documents. We focus on the task of automatic image retrieval to simulate common scenarios of humanities research, such as writer retrieval.</p> <p>The training data set encompasses images from (i) Letters A, where each writer contributed one or three images; (ii) Manuscripts, where each writer was represented by five consecutive images from a single book.<br> In total, it contains 300 writers contributing one page, 100 writers contributing three pages, and 120 writers contributing five pages resulting in 1200 images of 520 writers.</p> <p>The test data set contains 20 000 images: About 7 500 pages stem from isolated documents (partially anonymous writers, contributing one page each), and about 12 500 pages are from writers that contributed three or five pages.</p> <p> </p> <p>If you use this dataset, please cite:</p> <p>V. Christlein, A. Nicolaou, M. Seuret, D. Stutzmann, A. Maier: "ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents", in 15th International Conference on Document Analysis and Recognition, 2019, Sydney, Australia</p> <p> </p>
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