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5 results for “Document Embedding”
Supplementary Material for the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting
<p>This is the dataset used in the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting. </p> <p>It is composed of: </p> <p>- Pre-trained models using active learning for document screening on HealthCLEF and Epistemonikos datasets. </p> <p>- Epistemonikos and HealthCLEF datasets containing medical questions and relevant/non relevant articles. </p> <p>- Embeddings and Document Representations used for experiments on both datasets. </p> <p>Scripts to run experiments can be found at: <a href="https://github.com/afcarvallo/active_learning_document_screening">https://github.com/afcarvallo/active_learning_document_screening</a></p> <p> </p> <p><strong>Paper abstract:</strong></p> <p>Document screening is a fundamental task within Evidence-based Medicine (EBM), a practice that provides scientific evidence to support medical decisions. Several approaches have tried to reduce physicians' workload of screening and labeling vast amounts of documents to answer clinical questions. Previous works tried to semi-automate document screening, reporting promising results, but their evaluation was conducted on small datasets, which hinders generalization. Moreover, recent works in natural language processing have introduced neural language models, but none have compared their performance in EBM. In this paper, we evaluate the impact of several document representations such as TF-IDF along with neural language models (BioBERT, BERT, Word2vec, and GloVe) on an active learning-based setting for document screening in EBM. Our goal is to reduce the number of documents that physicians need to label to answer clinical questions. We evaluate these methods using both a small challenging dataset (HealthCLEF 2017) as well as a larger one but easier to rank (Epistemonikos). Our results indicate that word as well as textual neural embeddings always outperform the traditional TF-IDF representation. When comparing among neural and textual embeddings, in the HealthCLEF dataset the models BERT and BioBERT yielded the best results. On the larger dataset, Epistemonikos, Word2Vec and BERT were the most competitive, showing that BERT was the most consistent model across different corpuses. In term of active learning, an uncertainty sampling strategy combined with logistic regression achieved the best performance overall, above other methods under evaluation, and in fewer iterations.</p>
Embedding obtained in the paper "Two-dimensional visualization of large document libraries using t-SNE"
<p>Embedding obtained in the paper "Two-dimensional visualization of large document libraries using t-SNE" (<a href="https://openreview.net/forum?id=Hebl3EZ16lq">https://openreview.net/forum?id=Hebl3EZ16lq</a>, <a href="https://github.com/berenslab/pubmed-tsne-iclr">https://github.com/berenslab/pubmed-tsne-iclr</a>).</p> <p>The paper used the PubMed 2020 baseline originally obtained from <a href="https://www.nlm.nih.gov/databases/download/pubmed_medline.html">https://www.nlm.nih.gov/databases/download/pubmed_medline.html</a>, courtesy of the U.S. National Library of Medicine. That exact dataset is not available anymore. Now (June 2022) one can access the 2021 baseline, which includes our data and new works published in 2021, plus some revised and deleted citations. </p> <p>The dataset provided here includes:</p> <p>- from the PubMed database: article title, journal, PMID, and publication year.</p> <p>- produced by us: t-SNE embedding X and Y coordinates, label, and color.</p>
Crosslingual Document Embedding as Reduced-Rank Ridge Regression (Cr5)
<p>Crosslingual Document Embedding as Reduced-Rank Ridge Regression (Cr5)</p> <p>This repository contains the dataset of pretrained word embeddings in 28 languages, embedded in a shared latent space, released with the following <a href="http://dl.acm.org/authorize?N676969">paper</a>. The embeddings trained with the proposed approach are optimized for document level aggregation.</p> <blockquote> <p>"Crosslingual Document Embedding as Reduced-Rank Ridge Regression". Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. ACM, 2019.</p> </blockquote> <p>If you found the provided resources useful, please cite the above paper. Here's a BibTeX entry you may use:</p> <blockquote> <p>@inproceedings{josifoski-wsdm2019-cr5,<br> title={Crosslingual Document Embedding as Reduced-Rank Ridge Regression},<br> author={Josifoski, Martin and Paskov, Ivan S. and Paskov, Hristo S. and Jaggi, Martin and West, Robert},<br> booktitle={Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining},<br> organization={ACM},<br> year={2019}<br> }</p> </blockquote> <p><br> Any questions or suggestions?<br> Contact martin.josifoski@epfl.ch. </p>
Crosslingual Document Embedding as Reduced-Rank Ridge Regression (Cr5)
<p>Crosslingual Document Embedding as Reduced-Rank Ridge Regression (Cr5)</p> <p>This repository contains the pre-trained models released with the following <a href="http://dl.acm.org/authorize?N676969">paper</a>.</p> <blockquote> <p>"Crosslingual Document Embedding as Reduced-Rank Ridge Regression". Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. ACM, 2019.</p> </blockquote> <p>Cr5 embeds the words in a number of languages (2, 4 or 28, depending on the model), in a shared latent space. The repository contains the following models:</p> <ul> <li>English - Italian model, trained in a pairwise setting (prefixed pairwise_2_en-it)</li> <li>Danish - English model, trained in a pairwise setting (prefixed pairwise_2_da-en)</li> <li>Danish - Vietnamese model, trained in a pairwise setting (prefixed pairwise_2_da-vi)</li> <li>Danish - English - Italian - Vietnamese model, trained in a joint setting of 4 languages (prefixed joint_4)</li> <li>28 language model, trained in a joint setting of 28 languages (prefixed joint_28; the languages correspond to the following ISO 639 codes: bg, ca, cs, da, de, el, en, es, et, fi, fr, hr, hu, id, it, mk, nl, no, pl, pt, ro, ru, sk, sl, sv, tr, uk, vi.</li> </ul> <p>There is a tradeoff between the number of languages embedded in a single latent space and the performance achieved for each single language. Thus, in addition to the pre-trained models, the results from the document-level evaluation task (cf. Table 1 in <a href="https://infoscience.epfl.ch/record/263893/files/cr5%20paper.pdf">paper</a>) for the 28-language model are provided (when evaluating on the 4 languages considered in the paper, <a href="https://zenodo.org/record/2597441/files/joint_28_full_performance.pdf?download=1">joint_28_full_performance.pdf</a>). To choose the most optimal model for your use case, these results can be compared with the performance of the first four models, which are already evaluated in the paper.</p> <p>For using the pre-trained models, follow these steps:</p> <p>1. Download the model of choice for the desired languages (the dataset format is described in the readme)</p> <p>2. Download the helper library <a href="https://zenodo.org/record/2597441/files/cr5.py?download=1">cr5.py</a></p> <p>3. Look at <a href="https://zenodo.org/record/2597441/files/example.py?download=1">example.py</a> for an example on how to use the library</p> <p>If you found the provided resources useful, please cite the above paper. Here's a BibTeX entry you may use:</p> <blockquote> <p>@inproceedings{josifoski-wsdm2019-cr5,<br> title={Crosslingual Document Embedding as Reduced-Rank Ridge Regression},<br> author={Josifoski, Martin and Paskov, Ivan S. and Paskov, Hristo S. and Jaggi, Martin and West, Robert},<br> booktitle={Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining},<br> organization={ACM},<br> year={2019}<br> }</p> </blockquote> <p><br> Any questions or suggestions?<br> Contact martin.josifoski@epfl.ch. </p>
Dataset to accompany Clustering and Visualising Documents using Word Embeddings
<p>Dataset to accompany <em>Clustering and Visualising Documents using Word Embeddings</em>, a lesson for the Programming Historian.</p>
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