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94,588 Research products, page 1 of 9,459

  • Digital Humanities and Cultural Heritage
  • Research data
  • 2018-2022
  • Open Access

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  • Research data . 2022
    Open Access Italian
    Authors: 
    Shibingfeng, Zhang; Francesco, Fernicola; Federico, Garcea; Alberto, Barrón-Cedeño; Paolo, Bonora; Angelo, Pompilio;
    Publisher: Zenodo

    The corpus AriEmozione 2.0 contains a selection of operas composed between 1655 and 1765, with each verse annotated with an emotion. The annotation of AriEmozione 2.0 is conducted in a self-learning manner leveraging on the AriEmozione 1.0 corpus. Six emotion labels are used, namely Amore (Love) Gioia (Joy) Ammirazione (Admiration) Rabbia (Anger) Tristezza (Sadness) Paura (Fear) This corpus contains about 89k verses. Each line in the tsv file is composed of: Verse ID : unique aria and verse ID. Each ID is composed of an aria ID and a verse ID. For example, ZAP1590034_00 means the first verse of aria ZAP1590034 Verse text: the text of the verse in the aria Emotion: one of the six emotions AriEmozione 2.0 is a subset of the materials collected by project CORAGO. How to cite: @article{zhang2022ariemozione, title={AriEmozione 2.0: Identifying Emotions in Opera Verses and Arias}, author={ Zhang, Shibingfeng and Fernicola, Francesco and Garcea, Federico and Bonora, Paolo and Barr{\'o}n-Cede\~no, Alberto}, journal={Italian Journal of Computational Linguistics},volume={},issue_date = {} year={in press} } {"references": ["Zhang, S., Fernicola, F., Garcea, F., Bonora, P., Barr\u00f3n-Cede\u00f1o, A., (in press). AriEmozione 2.0: Identifying Emotions in Opera Verses and Arias. Italian Journal of Computational Linguistics"]}

  • Research data . 2022
    Open Access Italian
    Authors: 
    Shibingfeng, Zhang; Francesco, Fernicola; Federico, Garcea; Alberto, Barrón-Cedeño; Paolo, Bonora; Angelo, Pompilio;
    Publisher: Zenodo

    The corpus AriEmozione 2.0 contains a selection of operas composed between 1655 and 1765, with each verse annotated with an emotion. The annotation of AriEmozione 2.0 is conducted in a self-learning manner leveraging on the AriEmozione 1.0 corpus. Six emotion labels are used, namely Amore (Love) Gioia (Joy) Ammirazione (Admiration) Rabbia (Anger) Tristezza (Sadness) Paura (Fear) This corpus contains about 89k verses. Each line in the tsv file is composed of: Verse ID : unique aria and verse ID. Each ID is composed of an aria ID and a verse ID. For example, ZAP1590034_00 means the first verse of aria ZAP1590034 Verse text: the text of the verse in the aria Emotion: one of the six emotions AriEmozione 2.0 is a subset of the materials collected by project CORAGO. How to cite: @article{zhang2022ariemozione, title={AriEmozione 2.0: Identifying Emotions in Opera Verses and Arias}, author={ Zhang, Shibingfeng and Fernicola, Francesco and Garcea, Federico and Bonora, Paolo and Barr{\'o}n-Cede\~no, Alberto}, journal={Italian Journal of Computational Linguistics},volume={},issue_date = {} year={in press} }

  • Open Access Portuguese
    Authors: 
    Caus, Rodrigo; Baia, Arthur; Ávila, Victor; Hochgreb, Victor;
    Publisher: Zenodo

    The E-Commerce Question Answering Dataset (ECQuAD) is a reading comprehension dataset for question answering in brazilian e-commerce platforms. It consists of questions annotated by crowdworkers on a set of products' descriptions. It follows the SQuAD-v2 format, so questions might be unanswerable. This is a development set, for public usage, powered by GoBots.

  • Open Access
    Authors: 
    Quintus, Seth; Davis, Dylan S.; Cochrane, Ethan E.;
    Publisher: Zenodo

    This dataset provides supplemental information for the manuscript, "Diverse terracing practices revealed by automated lidar analysis across the Sāmoan islands", submitted to Archaeological Prospection. The dataset contains a trained Mask R-CNN deep learning model designed for detecting archaeological terracing features on the islands of American Samoa, associated training data, and the raw and cleaned output of detected terraces.

  • Open Access English
    Authors: 
    Benton, Adrian; Shi, Tianze; Irsoy, Ozan; Malioutov, Igor;
    Publisher: Zenodo

    This repository contains the evaluation sets used in A Benton, T Shi, O İrsoy, and I Malioutov."Weakly Supervised Headline Dependency Parsing". Findings of EMNLP. 2022. This dataset contains parse annotations for English news headlines and a script to produce conllu files joined with original headline text. Parse annotations are joined to the corresponding text by running: LDC_NYT_DIR="/PATH/TO/UNTARRED/LDC2008T19/" # path to untarred LDC2008T19 python build_eht.py --nyt_dir ${LDC_NYT_DIR} --num_proc 4 This will download the Google sentence compression (GSC) dataset, and build conllu files for GSC examples. If you have the New York Times Annotated Corpus (LDC2008T19) untarred locally, this will also join annotations to the NYT examples (location passed via --nyt_dir). Increase the argument to --num_procs to process more shards from the NYT corpus in parallel and reduce build time. The above was tested with python 3.9.7. The EHT evaluation sets, with gold-annotated POS tags and dependency relations, are built as EHT/gsc.test.conllu and EHT/nyt.test.conllu Silver, projected, trees which we used to train and validate out models are built under GSC_projected. These are not gold parse trees (projected predictions from the article lead sentence), and are shared purely for reproducibility sake. {"references": ["Weakly Supervised Headline Dependency Parsing. Adrian Benton, Tianze Shi,Ozan Irsoy,Igor Malioutov. Findings of EMNLP 2022."]}

  • Open Access
    Authors: 
    Pasqual, Valentina;
    Publisher: Zenodo

    The dataset contains a selection of works of art stored in Wikidata. It has been extracted via Wikidata API with action "wbgetentities"in date 01/05/2022. The dataset comprises literary productions, visual artworks, cultural sites, operatic works, performing arts, journals etc. We semiautomatically surveyed the existing Wikidata subclasses of work of art and removed all elements belonging to music and cinematographic domains as well as statistical rumor. In total, the dataset contains 2'156'363 works of art (wikidata entities) described by 18'924'333 statements and belonging to 6'612 selected classes. The dataset is stored in a zip folder organised in 10'782 json files, each storing 200 entities and their metadata.

  • Open Access French
    Authors: 
    Erwan SCHILD;
    Publisher: Zenodo

    [EN] French training dataset for chatbots dealing with usual requests on bank cards. Description: This dataset represents examples of common customer requests relating to bank cards management. It can be used as a training set for a small chatbot intended to process these usual requests. Content: The questions are asked in French. The dataset is divided into 10 intents of 100 questions each, for a total of 1 000 questions. Intents scope: Intents are constructed in such a way that all questions arising from the same intention have the same response or action. The scope covered concerns: loss or theft of cards; the swallowed card; the card order; consultation of the bank balance; insurance provided by a card; card unlocking; virtual card management; management of bank overdraft; management of payment limits; management of contactless mode. Origin: Intents scope is inspired by a chatbot currently in production, and the wording of the questions are inspired by the usual customers requests. [FR] Jeu d'entraînement en français d'assistants conversationnels traitant des demandes courantes sur les cartes bancaires. Description : Cet ensemble de données représente des exemples de demandes usuelles des clients concernant la gestion des cartes bancaires. Il peut être utilisé comme jeu d'entraînement pour un assistant conversationnel destiné à traiter ces demandes courantes. Contenu : Les questions sont formulées en français. L'ensemble de données est divisé en 10 intentions de 100 questions chacune, pour un total de 1 000 questions. Périmètre des intentions : Les intentions sont construites de telle manière que toutes les questions issues d'une même intention ont la même réponse ou action. Le périmètre couvert concerne : la perte ou le vol de cartes ; la carte avalée ; la commande des cartes ; la consultation du solde bancaire ; l'assurance fournie par une carte ; le déverrouillage de la carte ; la gestion de cartes virtuelles ; la gestion du découvert bancaire ; la gestion des plafonds de paiement ; la gestion du mode sans contact. Origine : Le périmètre des intentions est inspiré par un chatbot actuellement en production, et la formulation des questions est inspirée de demandes courantes de clients.

  • Open Access
    Authors: 
    Sims, Nathaniel;
    Publisher: Centre national de la recherche scientifique
  • Open Access English
    Authors: 
    Cardenas, Benjamin T.;
    Publisher: Zenodo

    This compilation contains data reported in the manuscript Cardenas, Lamb, Jobe, Mohrig, and Swartz, Morphodynamic preservation of fluvial channel belts. As of Nov 2022, this manuscript is submitted to SEPM (Society for Sedimentary Geology) journal The Sedimentary Record. Compilation contains: (1) Table showing the edge coordinates of each channel belt in the associated manuscript. (2) Table showing centerline point coordinates. (3) Table showing all width measurements for each channel belt. (4) A compilation table showing representative geometric measurements for each belt. (5) A python script to generate paleoflow directions from centerline coordinates. (6) A script to generate various geometric measurements from belt edge coordinates. (7) A script to plot histograms of geometric measurements.

  • Research data . Sound . 2022
    Open Access ero
    Authors: 
    Sims, Nathaniel;
    Publisher: Centre national de la recherche scientifique