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49 results for “word embedding”
Word Embedding of Amazon Product Review Corpus
<p>A word embedding of the <a href="https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#datasets">Amazon Product Review Corpus</a> (<a href="https://www.doi.org/10.1145/1341531.1341560">Jindal and Liu, 2008</a>).</p> <p>Created using <a href="https://code.google.com/archive/p/word2vec/">Word2Vec</a> in CBOW mode, 500 dimensions and window size 5.</p> <p>Words have been lemmatised and particle verbs have been merged into a single token (e.g. <code>calm_down</code>).</p> <ul> </ul> <p> </p> <p><strong>Attribution</strong></p> <p>This dataset was created as part of the following publication:</p> <p>Marc Schulder, Michael Wiegand, Josef Ruppenhofer and Benjamin Roth (2017). <strong>"Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features"</strong>. Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP). Taipei, Taiwan, November 27 - December 3, 2017. <a href="https://doi.org/10.5281/zenodo.3365609">DOI: 10.5281/zenodo.3365609</a>.</p> <p>If you use the data in your research or work, please cite the publication.</p>
Diachronic word embeddings from 19th-century newspapers digitised by the British Library (1800-1919)
<p>Word vectors related to the paper <em>Machines in the media: semantic change in the lexicon of mechanization in 19th-century British newspapers </em>by Nilo Pedrazzini and Barbara McGillivray (2022).</p> <p>The embeddings were trained on a 4.2-billion-word corpus of 19th-century British newspapers using Word2Vec and the following parameters:</p> <pre><code>sg = True min_count = 1 window = 3 vector_size = 200 epochs = 5</code></pre> <p>The embeddings are divided into periods of ten years each, with the vectors from each decade aligned to the ones from the most recent decade (1910s) using Orthogonal Procrustes.</p> <p>See related GitHub repository for the full documentation: <a href="https://github.com/Living-with-machines/DiachronicEmb-BigHistData">https://github.com/Living-with-machines/DiachronicEmb-BigHistData</a></p> <p>Project webpage (Living with Machines): <a href="https://livingwithmachines.ac.uk/">https://livingwithmachines.ac.uk/</a></p>
Word embeddings learnt on MEDLINE abstracts
<p>Accompanying a preprint manuscript and code repository, this folder contains both raw text data and learnt word embeddings. The data source is the set of MEDLINE articles published on or after 2000. Preprocessing consists of extraction of each article's title and abstract and some minor text processing. The result is a corpus of 10.5 million documents in a single 14 GB file. </p> <p>word2vec and fastText are used to learn word embeddings on this corpus and three sets of word embeddings are shared here: 1) word2vec skip-gram, 2) word2vec CBOW, and 3) fastText skip-gram. All three sets use the default parameters of the software (e.g. context=5) with the exception of hierarchical softmax optimization and dimension=200.</p> <p>Preprint manuscript: https://arxiv.org/abs/1705.06262<br> GitHub repository: https://github.com/vincentmajor/ctsa_prediction</p>
Classical Tibetan Word Embeddings
<p>Classical Tibetan word embeddings trained with FastText based on the 2018 version of the BDRC corpus, a segmented version of which is available on Zenodo:</p> <p>Meelen, Marieke, & Roux, Élie. (2020). The Annotated Corpus of Classical Tibetan (ACTib) - Version 2.0 (Segmented & POS-tagged) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3951503</p> <p>This is the first version trained with default FastText settings (100D) for a pilot study on Chinese-Tibetan crosslinguistic Semantic Textual Similarity:</p> <p>Felbur, Rafal, Marieke Meelen & Paul Vierthaler (2022), 'Crosslinguistic Semantic Textual Similarity of Buddhist Chinese and Classical Tibetan' in <em>Journal of Open Humanities Data</em>.</p> <p>This research was done with generous funding from the Open Philology project. This project (running 2018–2022) is funded by the European Research Council (ERC) under the Horizon 2020 program (Advanced Grant agreement No 741884). It is based at the Leiden University Institute for Area Studies.</p>
Deep Reference Mining from Scholarly Literature in the Arts and Humanities - Pre-trained word embeddings
<p>Pre-trained word vectors of dimensionality 100 and 300 for the publication: Deep Reference Mining from Scholarly Literature in the Arts and Humanities, submitted to Frontiers in Digital Humanities.</p> <p>The corpus of scholarly publications from which these vectors were trained is under copyright, therefore we publish these vectors for reproducibility. Please refer to the publication's repository for further details: <a href="https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing">https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing</a>.</p> <p>These vectors were trained using Gensim 3.1.0. The corpus was preprocessed as follows:</p> <ol> <li>word tokenization with NLTK word_punct tokenizer.</li> <li>digits were converted into the $NUM$ token</li> <li>words less frequent than 5 times, for every document, were converted to the $UNK$ token</li> <li>vectors were trained using the function: Word2Vec(window=5, min_count=5, sg=1)</li> </ol>
Diachronic and diatopic word embeddings from newspapers digitised by the British Library (1830-1889): North and South England
<p>Diachronic word embeddings (decade-level) trained with Word2Vec (via Gensim) on different geographic subcorpora of the Heritage Made Digital British and the Living with Machines historical newspaper collections:</p> <p>- North England (north.zip)</p> <p>- South England (south.zip)</p> <p>At the moment, for each subcorpus, Word2Vec models are available for each decade in the period 1830-1889. More models are on the way for the following:</p> <p>- each decade in the periods 1780-1829 and 1890-1920 for both North and South England.</p> <p>- diachronic models for the following regions: Scotland, Wales, and Midlands.</p> <p>The models were trained using the following parameters:</p> <pre><code>sg = True min_count = 1 window = 5 vector_size = 200 epochs = 5</code></pre> <p>Like the embeddings in <a href="https://doi.org/10.5281/zenodo.7181682">this repository</a>, the model for each decade was aligned to the most recent one with Orthogonal Procrustes.</p> <p>See related GitHub repository for the full documentation: <a href="https://github.com/Living-with-machines/DiachronicEmb-BigHistData">https://github.com/Living-with-machines/DiachronicEmb-BigHistData</a>.</p> <p>Project website (Living with Machines): <a href="https://livingwithmachines.ac.uk/">https://livingwithmachines.ac.uk/</a></p> <p>Data related to: Nilo Pedrazzini & Barbara McGillivray, <em>Diachronic and diatopic word embeddings from British historical newspapers</em>, presented at AIUCD (Convegno dell’Associazione per l’Informatica Umanistica e la Cultura Digitale) in Siena (Italy), June 2023.</p>
Luxembourgish word embedding (User comments from RTL.lu)
<p>This dataset is a word embedding model trained on Luxembourgish user comments from the media platform RTL.lu. It contains data from roughly 544k Luxembourgish texts published between December 2008 and December 2018. See the documentation file for detailed info.</p>
Bilingual English-German word embedding models for scientific text
<p>This data set contains three word embedding models, constructed from the same training corpus of English and German parallel scientific texts (abstracts and research project descriptions). All text was pre-processed by language-specific stemming with the Porter stemming algorithm, removing numbers, and lower-casing.</p> <p>The first model is a 1000-dimensional Latent Semantic Analysis model, constructed from concatenating the English and German texts. The input data was a m×n (297,852×923,864) document-term matrix of tf-idf weights. This was processed with truncated SVD. There are two files, the word vectors in file lsa_1000_Vmat.csv (the V* term by latent factors matrix of right singular values) and the dimension weights in lsa_1000_d_weights.csv (the 1000 values of the diagonal of the <span class="math-tex">\(\Sigma\)</span> matrix.</p> <p>lsa_1000_Vmat.csv has two fields, the term and its vector representation in LSA space, separated by a "|" character. The structure looks like this:</p> <p>tarifplural|{5.00599733151825e-08,-1.43071379136936e-08,8.32862290483082e-08,-6.08010721687266e-08,1.15831140150142e-07,-2.46470313387358e-08,3.43215595753282e-07,6.24301666802575e-07,-2.62907158945831e-07,-1.04120313981517e-07,4.5864574355164e-07,-2.31799632277312e-07,8.37354377858843e-07,8.22507467711628e-07,4.07585381069368e-07,-4.26358988941922e-08,-8.38652991154651e-07,1.98091851171759e-07,-3.94768548759816e-08,-4.28802181962385e-07, ...}</p> <p>The other two models are a basic Random Indexing and a Reflective Random Indexing model, contained in same file, RI_training.csv. Both models have 1000 dimensions. The data structure is as follows.</p> <ul> <li>language: either "en" (English) or "de" (German), the language of the term</li> <li>term: the term as a character string</li> <li>term_collection_count: integer, number of times the term occurred in the training data</li> <li>c_vector: vector of 1000 reals, RI context vector of the term. formatted like this: "{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.12309149,0,0,-0.12309149,0,0,0,0,0,0,0,0,0,0,0,0,0,0, ...}"</li> <li>n_docs: integer, number of different documents which contained the term</li> <li>c_vector_o2: vector of 1000 reals, RRI context vector of the term, formatted like c_vector above</li> </ul> <p>1,034,860 rows.</p> <p>All files are aggressively compressed with GNU gzip and will require much more disk space when uncompressed. Note the special formatting of the vector numeric variables, which are different for the two models.</p>
Thai Word Embeddings (word2vec) Trained on Oscar Corpus
<p>A large Thai word2vec model trained on Oscar corpus and tokenized and normalized with PyThaiNLP. The model can be loaded using gensim, it is saved in binary format. </p>
Early Irish Analogy Dataset for Word Embedding Evaluation
<p>An embedding evaluation dataset for Early Irish described in the paper "<a href="https://aclanthology.org/2023.insights-1.10.pdf">Do not Trust the Experts: How the Lack of Standard Complicates <span>NLP</span> for Historical <span>I</span>rish</a>".</p> <p>Traditionally, analogy datasets are based on pairwise semantic proportion, and therefore every question has a single correct answer. Given the high level of variation in historical languages, such a strict definition of a correct answer seems unjustified. Therefore, Early Irish Analogy Dataset follows the <a href="https://vecto.space/projects/BATS/">Bigger Analogy Test Set (BATS)</a> and provides several correct answers to each analogy question. </p> <p>Morphological and spelling variation data are extracted from the <a href="https://dil.ie/">eDIL</a>, a historical dictionary of medieval Irish. Unlike BATS, no distinction is made between inflection types due to eDIL's structure. The raw data amounted to 2,370 spelling variation and 9,690 morphological variation questions, from which 150 examples were randomly selected for each of the subsets to be comparable in size with the synonym and antonym subsets. The synonym and antonym subsets are translations of the correspondent BATS parts obtained by reverse-searching the eDIL and proofread by four expert evaluators. The dataset includes 98 entries in the synonym subset and 109 entries in the antonym subset, upon which three or more experts agreed.</p>
Buddhist Chinese Word Embeddings
<p>Buddhist Chinese word embeddings trained with FastText on the Buddhist texts present in the Kanseki repository.</p> <p>There are four models present here (and the full binary output for one), each differing in how the Kanseki repository was segmented into tokens. The "chinese_model" was segmented into 1-grams (individual characters). The "chinese_model_word" was segmented into words using a dictionary of buddhist terms and phrases not found were segmented with the classical Chinese word segmenter distributed with the stanza python library (https://stanfordnlp.github.io/stanza/available_models.html). The "chinese_model_hybrid_char_term" was segmented with a glossary of Buddhist terms and phrases not found were divided into 1-grams. "chinese_model_hybrid_char_term_2" uses a more extensive glossary of Buddhist terms and missing sections are divided into 1-grams.</p> <p>All models are default 100 dimensional FastText models created for this pilot study: Felbur, Rafal, Marieke Meelen & Paul Vierthaler (2022), 'Crosslinguistic Semantic Textual Similarity of Buddhist Chinese and Classical Tibetan' in <em>Journal of Open Humanities Data</em>.</p> <p>This research was done with generous funding from the Open Philology project. This project (running 2018–2022) is funded by the European Research Council (ERC) under the Horizon 2020 program (Advanced Grant agreement No 741884). It is based at the Leiden University Institute for Area Studies.</p>
Social Sciences Word Embeddings in FastText
<p>These social science word embeddings in FastText have been created from 37,604 open access social science research papers from the social science access repository (https://www.gesis.org/ssoar/home). They are available in German and English.</p> <p>(skipgram model, n-grams with n≥3 and n≤6, different dimensions (100, 150, 200, 300, 500), five epochs, learning rate 0.05, five negative examples)</p> <p>Please cite:</p> <p>Schiffers, Ricardo, Dagmar Kern, and Daniel Hienert. 2022. "Evaluation of Word Embeddings for the Social Sciences." In <em>Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature</em>, edited by Stefania Degaetano, Anna Kazantseva, Nils Reiter, and Stan Szpakowicz, 1-6. Gyeongju: Association for Computational Linguistics. <a href="https://aclanthology.org/2022.latechclfl-1.1">https://aclanthology.org/2022.latechclfl-1.1</a>.</p>
Tigrinya Analogy Test for evaluating Word Embeddings
<p><strong>Tigrinya Analogy Test for evaluating Word Embeddings</strong></p> <p>This is a Tigrinya version of the Google Analogy Test set, which is used to evaluate English word-embedding models. The analogy test is a well-established strategy to empirically evaluate the quality of word-embedding models. More information about the English task can be found at the <a href="https://aclweb.org/aclwiki/Google_analogy_test_set_(State_of_the_art)">ACL Wiki</a>.</p> <p>This data is was first machine translated then manually verified by a native speaker to reduce errors.</p> <p>Some aspects of the original analogy test is focused on English and may not transfer well to other languages, such as those related to grammar or morphology. Therefore, we have discarded examples that became irrelevant in Tigrinya when adapting the task. Finally, there are a total of <strong>18465</strong> entries in the Tigrinya Analogy Test set, while the source English data has <strong>19544</strong> entries.</p> <p>An entry is dropped if the translations led to one of the following conditions:</p> <ol> <li>If the source word pair map to one Tigrinya word, for example, lucky & luckiest both correspond to ዕድለኛ.</li> <li>If the source word results in a multi-word expression. For example, grandson (ወዲ ጓል / ወዲ ወዲ), granddaughter (ጓል ጓል / ጓል ወዲ). This because the typical word-embedding approaches such as <em>word2vec</em> are not designed to predict multi-word phrases.</li> </ol> <p> </p> <p><strong>Test Sections</strong></p> <p>The test includes a series of semantic and syntactic analogies divided up into subsections including world capitals, currencies, family, tense, and plurality. The test contains the following sections:</p> <ol> <li>capital-world</li> <li>currency</li> <li>city-in-state</li> <li>family</li> <li>gram1-adjective-to-adverb</li> <li>gram2-opposite</li> <li>gram3-comparative</li> <li>gram4-superlative</li> <li>gram5-present-participle</li> <li>gram6-nationality-adjective</li> <li>gram7-past-tense</li> <li>gram8-plural</li> <li>gram9-plural-verbs</li> </ol> <p> </p> <p><strong>Examples:</strong></p> <ul> <li>Semantic section of World Capitals: “ኣስመራ: ኤርትራ as ፓሪስ: ?” and if the model responds correctly it will return: “ፈረንሳ”.</li> <li>Semantic section of Family section: “ሰብኣይ: ሰበይቲ as ወዲ: ጓል”.</li> <li>Syntax section with tense, a sample analogy might be “Walk: Walked as Run: Ran”.<br> </li> </ul> <p><strong>Evaluation</strong></p> <p>The final accuracy of a model is the proportion of the questions that the model answers correctly.<br> Generally, a better-quality model would answer more questions correctly than a model of lower quality.<br> However, note that a model with low performance on this analogy test, might still contain useful information, but may not be robust or good enough for more complex tasks.</p> <p> </p> <p><strong>Limitations</strong></p> <ul> <li>The analogy test could be a good indicator of the quality of word-embeddings, but it should be used with caution when comparing models trained on varying domains of data. It shall not be expected to generalize equally to all domains.</li> <li>The final score can be affected by the size, vocabulary, and domain of the text with which the models are trained on. For example, this may not be a good benchmark to compare models trained on news text <em>vs</em> posts on social media.</li> <li>Even though a manual sanity check was performed, we note that the semi-automatic construction of the Tigrinya test set might contains errors. If you discover any, you are welcome to contribute back by either opening an <em>Issue</em> at the GitHub repo, <a href="https://github.com/fgaim/tigrinya-analogy-test">https://github.com/fgaim/tigrinya-analogy-test</a>.</li> </ul> <p> </p> <p><strong>Citation</strong></p> <p>If you use this resource in your research, please cite it accordingly.</p>
Word Embeddings for the Software Engineering Domain
<p>A .bin file for a word2vec model pre-trained on 15GB of Stack Overflow posts. </p> <p>For more details refer to the following paper:</p> <p>Efstathiou, V., Chatzilenas, C., Spinellis, D., 2018. "Word Embeddings for the Software Engineering Domain". In <em>Proceedings of the 15th International Conference on Mining Software Repositories.</em> ACM</p>
Spanish 3B words Word2Vec Embeddings
<p>Ready to use gensim Word2Vec embedding models for the Spanish language. Models are created using a window of +/- 5 words, discarding those words with less than 5 instances and creating a vector of 400 dimensions for each word. The text used to create the embeddings has been recovered from news, Wikipedia, the Spanish BOE, web crawling and open literary sources. The used text has a total of 3.257.329.900 words and 18.852.481.207 characters.</p> <p>We support two types of models: Gensim full models (complete_model.zip) and KeyedVectors (keyed_vectors.zip). You can check the differences between them in the following URL: <a href="https://radimrehurek.com/gensim/models/keyedvectors.html">https://radimrehurek.com/gensim/models/keyedvectors.html</a></p> <p>To load the full model use: model = Word2Vec.load("complete.model")<br> To load the KeyedVectors use: word_vectors = KeyedVectors.load('complete.kv', mmap='r')</p> <p>More info about the models can be found in: <a href="https://github.com/aitoralmeida/spanish_word2vec">https://github.com/aitoralmeida/spanish_word2vec</a></p>
Monthly word embeddings for Twitter random sample (English, 2012-2018)
<p>This dataset contains monthly word embeddings created from the tweets available via the statuses/sample endpoint of the Twitter Streaming API from 2012 to 2018. Full details of the creation of the dataset are given in <a href="https://www.aclweb.org/anthology/D19-1007/">Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings</a>. </p> <p>The md5sum of the gzipped tarball file is a76888ffec8cc7aebba09d365ca55ace .</p>
Spanish Legal Domain Word & Sub-Word Embeddings
<p><strong>Spanish Legal Word and Sub-word Embeddings in FastText</strong></p> <p>These embeddings have been generated from the largest corpus (9GB) ever made from Spanish Legal resources till the date.</p> <p>More legal domain resources: https://github.com/PlanTL-GOB-ES/lm-legal-es</p> <p><strong>Citation</strong></p> <pre><code>@misc{gutierrezfandino2021legal, title={Spanish Legalese Language Model and Corpora}, author={Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Aitor Gonzalez-Agirre and Marta Villegas}, year={2021}, eprint={2110.12201}, archivePrefix={arXiv}, primaryClass={cs.CL} }</code></pre> <p><strong>Copyright </strong></p> <p>Copyright (c) 2021 Secretaría de Estado de Digitalización e Inteligencia Artificial</p>
Spanish Skip-Gram Word Embeddings in FastText
<p>These Spanish word embeddings in FastText have been generated from the largest corpus ever made in Spanish till date. The corpus has more than 2TB of high-quality text, compiled from the different web crawlings done by the National Library of Spain from 2009 to 2019. </p> <p>These are the SKIP-GRAM embeddings, for the CBOW embeddings see: https://zenodo.org/record/5044988</p> <p><strong>Citation</strong></p> <pre><code>@article{gutierrezfandino2022, author = {Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquin Silveira-Ocampo and Casimiro Pio Carrino and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Aitor Gonzalez-Agirre and Marta Villegas}, title = {MarIA: Spanish Language Models}, journal = {Procesamiento del Lenguaje Natural}, volume = {68}, number = {0}, year = {2022}, issn = {1989-7553}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405}, pages = {39--60} }</code></pre> <p><strong>Copyright </strong></p> <p>Copyright (c) 2021 Secretaría de Estado de Digitalización e Inteligencia Artificial</p>
Spanish CBOW Word Embeddings in FastText
<p>These Spanish word embeddings in FastText have been generated from the largest corpus ever made in Spanish till date. The corpus has more than 2TB of high-quality text, compiled from the different web crawlings done by the National Library of Spain from 2009 to 2019. </p> <p>These are the CBOW embeddings, for the SKIP-GRAM embeddings see: https://zenodo.org/record/5046525</p> <p><strong>Citation</strong></p> <pre><code>@article{gutierrezfandino2022, author = {Asier Gutiérrez-Fandiño and Jordi Armengol-Estapé and Marc Pàmies and Joan Llop-Palao and Joaquin Silveira-Ocampo and Casimiro Pio Carrino and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Aitor Gonzalez-Agirre and Marta Villegas}, title = {MarIA: Spanish Language Models}, journal = {Procesamiento del Lenguaje Natural}, volume = {68}, number = {0}, year = {2022}, issn = {1989-7553}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405}, pages = {39--60} }</code></pre> <p><strong>Copyright </strong></p> <p>Copyright (c) 2021 Secretaría de Estado de Digitalización e Inteligencia Artificial</p>
Obtaining Better Static Word Embeddings Using Contextual Embedding Models
<p><strong>Obtaining Better Static Word Embeddings Using Contextual Embedding Models</strong></p> <p>This repository contains the dataset of pretrained word embeddings as well as datasets used to train them, released with the following <a href="https://arxiv.org/pdf/2106.04302.pdf">paper</a>.</p> <blockquote> <p>“Obtaining Better Static Word Embeddings Using Contextual Embedding Models” <em>ACL</em> (2021).</p> </blockquote> <p>The wikipedia datasets were preprocessed from the wikipedia dump downloaded from <a href="http://dumps.wikimedia.org">dumps.wikimedia.org</a> under Creative Commons Attribution-Share-Alike 3.0 License .</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{Gupta2021ObtainingPC,<br> title={Obtaining Better Static Word Embeddings Using Contextual Embedding Models},<br> author={Prakhar Gupta and Martin Jaggi},<br> booktitle={ACL},<br> year={2021}<br> }</p> </blockquote>
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.