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475 results for “words”
Word Retrieval in the Wild in People With Post-Stroke Aphasia
ClinicalTrials.gov study NCT05338216. IPD Sharing: YES. Countries: 1. Publications: 19.
LOOKing for Multi-word Expressions in American Sign Language
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Data from: Shared and modality-specific brain regions that mediate auditory and visual word comprehension
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War of Words: The Competitive Dynamics of Legislative Processes
<p><strong>Update: A newer version of this dataset is available <a href="https://zenodo.org/record/4709248#.YXesJS8itqs">here</a>.</strong><strong> </strong>It comes with features extracted from the MEPs, the edits, and the dossiers, such as the <strong>nationality</strong> of MEPs, the <strong>type of law </strong>being edited, and the <strong>text of the edits</strong>. Check it out!</p> <p>This upload contains the dataset presented and used in the paper:</p> <blockquote> <p>Kristof, V., Grossglauser, M., Thiran, P., <a href="https://infoscience.epfl.ch/record/275473/"><em>War of Words: The Competitive Dynamics of Legislative Processes</em></a>, The Web Conference, April 20-24, 2020, Taipei, Taiwan</p> </blockquote> <p><strong>Read Section 2.2 of the paper to learn more about the European legislative process. </strong>The code to process and use the dataset can be found on <a href="https://github.com/indy-lab/war-of-words">GitHub</a>.</p> <p>The dataset is split into two legislature periods of the European Parliament, the 7th (<strong>war-of-words-ep7.txt</strong>) and the 8th (<strong>war-of-words-ep8.txt</strong>) legislature. Here is a snippet to load the dataset (for EP7 in this example) in Python:</p> <pre><code class="language-python">import json with open('path/to/war-of-words-ep7.txt') as f: dataset = [json.loads(l) for l in f.readlines()] </code></pre> <p>In the two text files, each line is a data point representing a <em>conflict between edits</em>. It is encoded as a JSON list of dictionaries, where each dictionary is an edit. Each edit has the following structure:</p> <pre><code class="language-json">{ 'edit_id': 163187, // Unique edit identifier. 'accepted': True, // Label. 'dossier_ref': 'ENVI-AD(2012)487738', // Reference to dossier (see below). 'authors': [ // List of authors. { 'id': 4550, // Unique MEP identifier (see below). 'name': 'Jill EVANS', // MEP name. 'rapporteur': False // Whether the MEP is rapporteur. }, ], }</code></pre> <p>You can assume that:</p> <ul> <li>Each data point has at least one edit.</li> <li>If there is only one edit, then it is <em>in conflict with the status quo </em>(see Section 4 of the paper).</li> <li>If there are two or more edits in conflict, then they are all in conflict against each other <em>and</em> they are in conflict with the status quo (see Section 4 of the paper).</li> <li>At most one edit is accepted in each data point.</li> <li>In each legislature, each edit has a unique identifier.</li> <li>There are no timestamps associated with edits (see Section 3 of the paper). </li> </ul> <p>The <strong>dossier_ref</strong> can be used to get more information on the dossier. It is formatted as <strong>COMM-TYPE(YEAR)PENUMBER </strong>(this follows the notation of file names used by the Parliament Secretariat), where</p> <ul> <li><strong>COMM</strong> is the <a href="https://www.europarl.europa.eu/committees/en/about/introduction">committee</a> identifier (4 capital letters)</li> <li><strong>TYPE</strong> is either <strong>AD</strong> (opinion) or <strong>A{7,8}</strong> (report for EP7 or EP8, see Section 2.2 of the paper)</li> <li><strong>YEAR</strong> is the year the dossier has been voted</li> <li><strong>PENUMBER</strong> is the "PE number", a document identifier used by the European Parliament</li> </ul> <p>You can browse the Parliament documents to find details about the dossier for <a href="https://www.europarl.europa.eu/committees/en/archives/7/document-search">EP7</a> and <a href="https://www.europarl.europa.eu/committees/en/archives/8/document-search">EP8</a> (the PE number field should be enough).</p> <p>The parliamentarians (MEPs, for Member of the European Parliament) have a unique identifier that you can use to get more details about them on the Parliament website: Go to <strong>https://www.europarl.europa.eu/meps/en/MEP_ID</strong>, where <strong>MEP_ID </strong>is the id of the MEP of interest.</p> <p>This dataset is vowed to become richer: I will add more features, as I am able to extract them.</p> <p> </p> <p><strong>Don't hesitate to <a href="mailto:victor.kristof@epfl.ch?subject=Question%20about%20the%20War%20of%20Words%20dataset">reach out to me</a> if you have any questions!</strong></p> <p> </p> <p>To cite this work:</p> <pre><code>@inproceedings{kristof2020war, author = {Kristof, Victor and Grossglauser, Matthias and Thiran, Patrick}, title = {War of Words: The Competitive Dynamics of Legislative Processes}, year = {2020}, booktitle = {Proceedings of The Web Conference 2020}, pages = {2803–2809}, numpages = {7}, location = {Taipei, Taiwan}, series = {WWW '20} }</code></pre> <p> </p>
Data and Code Accompanying the Study "Developing an annotation framework for word formation processes in comparative linguistics"
<p>Cite the source of the dataset as:</p> <blockquote> <p>Schweikhard, N. E. and List, J.-M. (2020): Developing an annotation framework for word formation processes in comparative linguistics. SKASE Journal of Theoretical Linguistics. 17.1: 2-26.</p> </blockquote>
SemEval-2020 Task 3: Graded Word Similarity in Context
<p>For this tasks we ask participants to build systems that try to predict the effect that context has in human perception of similarity of words.</p> <p>We have seen very interesting work that uses local context to predict <strong>discrete</strong> changes in meaning: the different senses of a polysemous word. However context also has more subtle, <strong>continuous</strong> (<strong>graded</strong>) effects on meaning, even for words not necessarily considered polysemous.</p> <p>In order to be able to look at these effects we are building several datasets where we ask annotators to score how similar a pair of words are after they have read a short paragraph (which contains the two words). Each pair is scored within two of these paragraphs, allowing us to look at changes in similarity ratings due to context.</p> <p>CodaLab was used to run this task, you can see the dedicated website and the results of the participants at: https://competitions.codalab.org/competitions/20905</p>
Do Word Embeddings Capture Spelling Variation?
<p>When using the data, please cite:</p> <p><em>"Do Word Embeddings Capture Spelling Variation?". Dong Nguyen and Jack Grieve, COLING 2020.</em></p> <p>The data contains:</p> <ul> <li>the trained embeddings (embeddings-reddit.tgz and embeddings-twitter.tgz), 50-300 dimensions</li> <li>the analysis/output files (data.tgz).</li> </ul> <p>See also the Github repository: https://github.com/dongpng/coling2020.</p>
Investigating Transformers as Context-aware Word Search Engines - Data and Code
<p>This is the review version, please use the published record: </p> <p> https://zenodo.org/record/6425595</p> <p> </p> <p>Data and code for the paper 'Investigating Transformers as Context-aware Word Search Engines' as submitted for review at ACL21. </p> <p> </p>
Life Story in 11,000 words
For more information about this item visit: https://bhpsite.org/ Source: Objaverse 1.0 / Sketchfab
Phrase-Level Word Embeddings
<p>Phrase-level word embeddings for sentiment analysis.</p>
Hacker News lda2vec model word vectors
<p>See also: https://zenodo.org/record/45901 and https://zenodo.org/record/49899</p>
Compound words in Arabic and Chinese
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Citizen Science Assoziationen Word Cloud
<p>Assoziationen, die Teilnehmer*innen des Forums Citizen Science (November 2023 in Freiburg, <a href="https://www.buergerschaffenwissen.de/veranstaltungen/forum-citizen-science-2023">https://www.buergerschaffenwissen.de/veranstaltungen/forum-citizen-science-2023</a>) mit dem Begriff Citizen Science in Verbindung brachten. Dargestellt als Word Cloud mit Inkscape.</p> <p>Beantwortet wurde die Frage “Welche drei Begriffe fallen dir zu Citizen Science ein?” schriftlich von 31 Personen. Es wurden 97 Antworten genannt, davon 72 unterschiedliche. Sehr lange Antworten wurden für die grafische Darstellung vereinfacht (insgesamt zwei Begriffe, siehe Dokument Citizen Science Assoziationen-Datensammlung_12-03-24.xlsx).</p> <p>Die Word Cloud-Visualisierung darf beliebig unter folgender Angabe verwendet werden: "Tim Kiessling (CC BY NC 4.0), https://doi.org/10.5281/zenodo.10810212". Die Vektor-Grafikdatei ist verfügbar um Änderungen durchzuführen (Citizen Science Assoziationen_12-03-24.svg). </p>
Word Cloud of Sky News Australia's Video Comments
<p>Word Cloud graph created on Python with a Google Colab Notebook. Dataset of YouTube comments of Sky News Australia's video about France Abortion Law as a constitutional right, collected from Communalytic. </p>
Word Cloud of CBS US' Video Comments
<p>Word Cloud graph created on Python with a Google Colab Notebook. Dataset of YouTube comments of CBS US' video about France's Abortion Law made as a constitutional right, collected from Communalytic. </p>
Exploring the Determinants of Repurchase Intention and Word of Mouth Intention in Social Commerce
<p>This data is research data entitled Exploring the Determinants of Repurchase Intention and Word of Mouth Intention in Social Commerce</p>
Dataset of Move, Rove, Love: Color Cues Help Learning Novel English Words When Pronunciation Is Not Predictable From Spelling
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DWUG IT: Diachronic Word Usage Graphs for Italian
<p>This data collection contains diachronic Word Usage Graphs (WUGs) for Italian. Find a description of the data format, code to process the data and further datasets on the <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUGsite</a> and in the paper:<br><br></p> <div>P. Cassotti, P. Basile, and N. Tahmasebi, ‘DWUGs-IT: Extending and Standardizing Lexical Semantic Change Detection for Italian’, in <em>Proceedings of the Tenth Italian Conference on Computational Linguistics (CLiC-it 2024), Pisa, Italy, December 4-6, 2024</em>, 2024, vol. 3878.<br><a href="https://ceur-ws.org/Vol-3878/22_main_long.pdf">https://ceur-ws.org/Vol-3878/22_main_long.pdf</a></div>
SemEval-2013 Task 13: Word Sense Induction for Graded and Non-Graded Senses
<p>Data originally provided by the SemEval-2013 Task 13 organizers and which was available through the following link:</p> <p>https://www.cs.york.ac.uk/semeval-2013/task13/data/uploads/semeval-2013-task-13-test-data.zip</p>
SemEval-2010 Task 14: Word Sense Induction & Disambiguation
<p>Data originally provided by the SemEval-2010 Task 14 organizers and which was available through the following links:</p> <p>1. https://www.cs.york.ac.uk/semeval2010_WSI/files/evaluation.zip<br> 2. https://www.cs.york.ac.uk/semeval2010_WSI/files/training_data.tar.gz<br> 3. https://www.cs.york.ac.uk/semeval2010_WSI/files/test_data.tar.gz</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.