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15 results for “Greek language”
Greek Text to Trajectories Sign Language Dataset
<p>Entails the 2D human pose trajectories of Greek Elementary Sign Language Dataset and Greek News Sign Language Dataset (31681 examples).</p>
Greek News Sign Language Dataset - Part A
<p>Part A entails 1.000 signed phrases of crime-related news stories broadcasted in Greece.</p>
Greek News Sign Language Dataset - Part B
<p>Part B entails 989 signed phrases of crime-related news stories broadcasted in Greece.</p>
Greek Elementary Sign Language Dataset
<p>Entails the course material of the first years of elementary school in Greece. It includes 29.653 signed phrases that are present in the 33 issues of 13 distinct textbooks of the A, B and C years of Primary school.</p> <p>The Elementary Dataset consists of the following courses:</p> <p>9499 videos of Greek Language (1st, 2nd and 3rd year)<br> 6581 videos of Mathematics (1st, 2nd and 3rd year)<br> 4158 videos of Anthology of Greek Literacy (1st, 2nd, 3rd and 4th year)<br> 5521 videos of Environmental Studies (1st, 2nd and 3rd year)<br> 2067 videos of History (3rd year)<br> 1825 Videos of Religious Study (3rd year)</p> <p><br> Version 3 - Major Features and Improvements</p> <p>Converted Video format and Video FPS<br> Removed English words and characters<br> Improved Transcriptions</p>
A collection of Sign Language Narations by Virtual Humans in the Greek Sign Language
<p>A collection of Sign Language Narations by Virtual Humans in the Greek Sign Language in the context of the Mastic Pilot of Mingei.</p>
PrescIT_T2EDK00640_Survey_Responses_Greek_language
<p>The dataset includes 430 collected answers from self-reported online distributed questionnaires regarding healthcare professionals views on e-prescription systems in Greece. The survey was conducted in the context of the PrescIT project (https://www.prescit.com/), aiming to elicit end-user requirements for the developed PrescIT e-prescription and clinical decision support platform. The dataset is currently available in the Greek language. </p> <p>Results from the analysis of the dataset have been presented in the following publication:</p> <p>- Grammatikopoulou, M., Lazarou, I., Giannios, G., Kakalou, C.A., Zachariadou, M., Zande, M., Karanikas, H., Thireos, E., Stavropoulos, T.G., Natsiavas, P., Nikolopoulos, S., Kompatsiaris, I., Electronic prescription systems in Greece: a large-scale survey of healthcare professionals’ perceptions. Arch Public Health 82, 68 (2024). https://doi.org/10.1186/s13690-024-01304-6</p>
Ancient Greek language models
<p>In this repository, we release a series of vector space models of Ancient Greek, trained following different architectures and with different hyperparameter values. </p> <p>Below is a breakdown of all the models released, with an indication of the training method and hyperparameters. The models are split into ‘<strong>Diachronica’ </strong>and ‘<strong>ALP’ </strong>models, according to the published paper they are associated with.</p> <blockquote> <p>[<strong>Diachronica</strong>:] Stopponi, Silvia, Nilo Pedrazzini, Saskia Peels-Matthey, Barbara McGillivray & Malvina Nissim. Forthcoming. Natural Language Processing for Ancient Greek: Design, Advantages, and Challenges of Language Models, <em>Diachronica</em>.</p> <p>[<strong>ALP</strong>:] Stopponi, Silvia, Nilo Pedrazzini, Saskia Peels-Matthey, Barbara McGillivray & Malvina Nissim. 2023. Evaluation of Distributional Semantic Models of Ancient Greek: Preliminary Results and a Road Map for Future Work. <em>Proceedings of the Ancient Language Processing Workshop associated with the 14th International Conference on Recent Advances in Natural Language Processing (RANLP 2023)</em>. 49-58. Association for Computational Linguistics (ACL). https://doi.org/10.26615/978-954-452-087-8.2023_006</p> </blockquote> <h1><em>Diachronica</em> models</h1> <h2>Training data</h2> <p>Diorisis corpus (Vatri & McGillivray 2018). Separate models were trained for:</p> <ol> <li>Classical subcorpus</li> <li>Hellenistic subcorpus</li> <li>Whole corpus</li> </ol> <p>Models are named according to the (sub)corpus they are trained on (i.e. <code>hel_</code> or <code>hellenestic</code> is appended to the name of the models trained on the Hellenestic subcorpus, <code>clas_</code> or <code>classical</code> for the Classical subcorpus, <code>full_</code> for the whole corpus).</p> <h2>Models</h2> <h3><strong><em>Count-based</em></strong></h3> <blockquote> <p>Software used: LSCDetection (Kaiser et al. 2021; <a href="https://github.com/Garrafao/LSCDetection">https://github.com/Garrafao/LSCDetection</a>)</p> </blockquote> <p>a. With Positive Pointwise Mutual Information applied (folder PPMI spaces). For each model, a version trained on each subcorpus after removing stopwords is also included (<code>_stopfilt</code> is appended to the model names). Hyperparameter values: <code>window=5</code>, <code>k=1</code>, <code>alpha=0.75</code>.</p> <p>b. With both Positive Pointwise Mutual Information <em>and</em> dimensionality reduction with Singular Value Decomposition applied (folder PPMI+SVD spaces). For each model, a version trained on each subcorpus after removing stopwords is also included (<code>_stopfilt</code> is appended to the model names). Hyperparameter values: <code>window=5</code>, <code>dimensions=300</code>, <code>gamma=0.0</code>.</p> <h3><strong><em>Word2Vec</em></strong></h3> <blockquote> <p>Software used: CADE (Bianchi et al. 2020; <a href="https://github.com/vinid/cade">https://github.com/vinid/cade</a>).</p> </blockquote> <p>a. Continuous-bag-of-words (CBOW). Hyperparameter values: <code>size=30</code>, <code>siter=5</code>, <code>diter=5</code>, <code>workers=4</code>, <code>sg=0</code>, <code>ns=20</code>.</p> <p>b. Skipgram with Negative Sampling (SGNS). Hyperparameter values: <code>size=30</code>, <code>siter=5</code>, <code>diter=5</code>, <code>workers=4</code>, <code>sg=1</code>, <code>ns=20</code>.</p> <h3><strong><em>Syntactic word embeddings</em></strong></h3> <p>Syntactic word embeddings were also trained on the Ancient Greek subcorpus of the PROIEL treebank (Haug & Jøhndal 2008), the Gorman treebank (Gorman 2020), the PapyGreek treebank (Vierros & Henriksson 2021), the Pedalion treebank (Keersmaekers et al. 2019), and the Ancient Greek Dependency Treebank (Bamman & Crane 2011) largely following the SuperGraph method described in Al-Ghezi & Kurimo (2020) and the Node2Vec architecture (Grover & Leskovec 2016) (see <a href="https://github.com/npedrazzini/ancientgreek-syntactic-embeddings#graph-based-syntactic-word-embeddings">https://github.com/npedrazzini/ancientgreek-syntactic-embeddings</a> for more details). Hyperparameter values: window=1, min_count=1.</p> <h1><em>ALP</em> models</h1> <h2>Training data</h2> <p>Archaic, Classical, and Hellenistic portions of the Diorisis corpus (Vatri & McGillivray 2018) merged, stopwords removed according to the list made by Alessandro Vatri, available at https://figshare.com/articles/dataset/Ancient_Greek_stop_words/9724613.</p> <h2>Models</h2> <h3><strong><em>Count-based</em></strong></h3> <blockquote> <p>Software used: LSCDetection (Kaiser et al. 2021; <a href="https://github.com/Garrafao/LSCDetection">https://github.com/Garrafao/LSCDetection</a>) </p> </blockquote> <p>a. With Positive Pointwise Mutual Information applied (folder ppmi_alp). Hyperparameter values: <code>window=5</code>, <code>k=1</code>, <code>alpha=0.75</code>. Stopwords were removed from the training set.</p> <p>b. With both Positive Pointwise Mutual Information <em>and</em> dimensionality reduction with Singular Value Decomposition applied (folder ppmi_svd_alp). Hyperparameter values: <code>window=5</code>, <code>dimensions=300</code>, <code>gamma=0.0</code>. Stopwords were removed from the training set.</p> <h3><strong><em>Word2Vec</em></strong></h3> <blockquote> <p>Software used: Gensim library (Řehůřek and Sojka, 2010)</p> </blockquote> <p>a. Continuous-bag-of-words (CBOW). Hyperparameter values: <code>size=30</code>, <code>window=5</code>, <code>min_count=5</code>, <code>negative=20</code>, <code>sg=0</code>. Stopwords were removed from the training set.</p> <p>b. Skipgram with Negative Sampling (SGNS). Hyperparameter values: <code>size=30</code>, <code>window=5</code>, <code>min_count=5</code>, <code>negative=20</code>, <code>sg=1</code>. Stopwords were removed from the training set.</p> <h1><strong>References</strong></h1> <p>Al-Ghezi, Ragheb & Mikko Kurimo. 2020. Graph-based syntactic word embeddings. In Ustalov, Dmitry, Swapna Somasundaran, Alexander Panchenko, Fragkiskos D. Malliaros, Ioana Hulpuș, Peter Jansen & Abhik Jana (eds.), <em>Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)</em>, 72-78.</p> <p>Bamman, D. & Gregory Crane. 2011. The Ancient Greek and Latin dependency treebanks. In Sporleder, Caroline, Antal van den Bosch & Kalliopi Zervanou (eds.), <em>Language Technology for Cultural Heritage. Selected Papers from the LaTeCH [Language Technology for Cultural Heritage] Workshop Series. Theory and Applications of Natural Language Processing</em>, 79-98. Berlin, Heidelberg: Springer.</p> <p>Gorman, Vanessa B. 2020. Dependency treebanks of Ancient Greek prose. <em>Journal of Open Humanities Data</em> 6(1).</p> <p>Grover, Aditya & Jure Leskovec. 2016. Node2vec: scalable feature learning for networks. In <em>Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘16)</em>, 855-864.</p> <p>Haug, Dag T. T. & Marius L. Jøhndal. 2008. Creating a parallel treebank of the Old Indo-European Bible translations. In <em>Proceedings of the Second Workshop on Language Technology for Cultural Heritage Data (LaTeCH)</em>, 27–34.</p> <p>Keersmaekers, Alek, Wouter Mercelis, Colin Swaelens & Toon Van Hal. 2019. Creating, enriching and valorizing treebanks of Ancient Greek. In Candito, Marie, Kilian Evang, Stephan Oepen & Djamé Seddah (eds.), <em>Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories</em> <em>(TLT, SyntaxFest 2019)</em>, 109-117.</p> <p>Kaiser, Jens, Sinan Kurtyigit, Serge Kotchourko & Dominik Schlechtweg. 2021. Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection. In <em>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics</em>.</p> <p>Schlechtweg, Dominik, Anna Hätty, Marco del Tredici & Sabine Schulte im Walde. 2019. A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains. In <em>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</em>, 732-746, Florence, Italy. ACL.</p> <p>Vatri, Alessandro & Barbara McGillivray. 2018. The Diorisis Ancient Greek Corpus: Linguistics and Literature. <em>Research Data Journal for the Humanities and Social Sciences</em> 3, 1, 55-65, Available From: Brill <a href="https://doi.org/10.1163/24523666-01000013" target="_blank" rel="noopener">https://doi.org/10.1163/24523666-01000013</a></p> <p>Vierros, Marja & Erik Henriksson. 2021. PapyGreek treebanks: a dataset of linguistically annotated Greek documentary papyri. <em>Journal of Open Humanities Data</em> 7.</p>
Dataset for the paper "Ideology and identity in grammar: A diachronic-quantitative approach to language standardisation processes in Ancient Greek"
<p>This is the dataset for the paper "Ideology and identity in grammar: A diachronic-quantitative approach to language standardisation processes in Ancient Greek"</p>
Translation and validation of the Body-Mind-Spirit Wellness Behavior and Characteristic Inventory in the Greek language
<p>This investigation was guided by Hettler's 1979 Six Dimensions of Wellness Model. Dimensions of physical, emotional, intellectual, occupational, social, and spiritual wellness were identified. The Body-Mind-Spirit Wellness Behavior and Characteristic Inventory was used to assess this (BMS-WBCI). The purpose of this study was to further validate the BMS-WBCI by reporting reliability as the scale's internal consistency when used to measure wellness in a sample of aged Greek people. a descriptive cross-sectional design was used for the sample collection. A random sample (n =520) from the Greek general population participated voluntarily and anonymously in the study including 28,8% males and 71,2% females. Their mean age of the sample was 39.86 years (SD = 10.5), ranging from 16 to 75 years .The BMS-WBCI consists of 44 items which are divided into three subscales (Hey, Calderon, & Carroll, 2006). The first subscale is labeled “<strong>body</strong>” and consists of nine items (items 1 – 9) relating to risk behaviors including physical fitness, personal safety and dietary intake, covering the physical domain of wellness. The second subscale is labeled “<strong>mind</strong>” and consists of 20 items (items 10 – 29) representing the intellectual, social, emotional, and occupational domains of wellness. Finally, the third subscale is labeled “<strong>spirit</strong>” and consists of 15 items (items 30 – 44) covering the spiritual, emotional, and occupational domains of wellness. Each of the 44 items asks the participant to respond on a 3-point Likert scale where 1 indicates “rare/seldom,” 2 “occasionally/sometimes,” and 3 “often/always.” Possible scores for the entire scale range from a minimum of 44 to a maximum of 132. Higher scores designate a higher level of participation in positive health behaviors and agreement with characteristics that contribute to overall well-being. </p>
Greek Elementary Sign Language Dataset scripts for loading dataset
<p>Entails py.scpripts for downloading the course material, loading video and text datasets, for each course of “Greek Elementary Sign Language Dataset”.</p>
GSLW - Greek Sign Language in the Wild Dataset
<p>GSLW: The dataset has been recorded under various background and lighting variations and with different smartphones. The camera position and orientation are gently varied among subsequent recordings to increase video diversity. <strong>15 cases</strong> of hearing-impaired people dealing with public services have been recorded. The resulting test dataset has <strong>1,736 videos</strong> that were annotated both at individual gloss and sentence level.<br> <br> Instructions:<br> <br> <strong><em> GSLW.csv</em></strong> file has the paths, gloss and sentence annotations of the videos.</p> <p><strong><em> load_gslw.py</em></strong> has a python function to load the GSLW.csv file for training or inference.<br> <br> Citation: <br> If you use our dataset please cite our work :</p> <pre>@article{gsl, author={N. M. {Adaloglou} and T. {Chatzis} and I. {Papastratis} and A. {Stergioulas} and G. T. {Papadopoulos} and V. {Zacharopoulou} and G. {Xydopoulos} and K. {Antzakas} and D. {Papazachariou} and P. n. {Daras}}, journal={IEEE Transactions on Multimedia}, title={A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition}, year={2021}, volume={}, number={}, pages={1-1}, doi={10.1109/TMM.2021.3070438}}</pre>
Greek Elementary Sign Language Dataset
<p>Entails the course material of the first years of elementary school in Greece. It includes 29.698 signed phrases that are present in the 33 issues of 13 distinct textbooks of the A, B and C years of Primary school.</p> <p>The Elementary Dataset consists of the following courses:</p> <ul> <li>9507 videos of Greek Language (1st, 2nd and 3rd year)</li> <li>6599 videos of Mathematics (1st, 2nd and 3rd year)</li> <li>4163 videos of Anthology of Greek Literacy (1st, 2nd, 3rd and 4th year)</li> <li>5528 videos of Environmental Studies (1st, 2nd and 3rd year)</li> <li>2069 videos of History (3rd year)</li> <li>1832 Videos of Religious Study (3rd year)</li> </ul> <p>Version 2 - Major Features and Improvements</p> <ul> <li>Removed Duplicated Videos</li> <li>Improved Transcriptions</li> <li>Audio extraction (for Greek STT tasks)</li> </ul>
Greek Text to 3D Trajectories Sign Language Dataset
<p>Entails the 3D human pose trajectories of the Greek Elementary Sign Language Dataset.</p>
Validation of the Catquest-9SF Questionnaire in Greek Language
ClinicalTrials.gov study NCT05323526. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Development and Validation of a Digital Optotype for Near Vision in Greek Language.
ClinicalTrials.gov study NCT04242836. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.