cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57771/g94w-8j31
title: ABSITA dataset
title: ABSITA

description: The ABSITA dataset contains 4,121 reviews written in Italian and collected from the website booking.com. Reviews are annotated according to seven aspects: pulizia (cleanliness), comfort, servizi (amenities), staff, qualita-prezzo (value), wifi (wireless Internet connection) and posizione (location). For each aspect, the polarity (positive, negative) of its mention has been annotated. The positive and negative polarities are annotated independently, thus for each aspect four sentiment classes are possible: positive (positive=1, negative=0), negative (positive=0, negative=1), neutral (positive=0, negative=0), mixed (positive=1, negative=1).<p>The dataset has been created and used in the context of the ABSITA task (http://sag.art.uniroma2.it/absita/),  organised as part of EVALITA 2018 (http://www.evalita.it/2018).
DOI: 10.57771/ymy1-3q49
title: AcCompl-it Dataset
title: AcCompl-it

description: The AcCompl-It dataset comprehends the Complexity and the Acceptability Datasets. The first data set is composed of  2,530 Italian sentences annotated with human judgments of perceived complexity on a 7–point Likert scale; the second one consists of 1,683 Italian sentences annotated with human judgments of acceptability on a 7–point Likert scale.<p>The datasets were created and used in the context of AcCompl-It (https://sites.google.com/view/accompl-it/), a shared task for Italian proposed for the 2020 EVALITA campaign (http://www.evalita.it/2020). For the purpose of the task, both datasets were split into training and validation samples with a proportion of 80% to 20%, respectively.<p>
DOI: 10.57771/h45w-cp76
title: AMI 2018 Dataset
title: AMI 2018

description: The AMI 2018 dataset collects 5,000 tweets written in Italian and 5,000 in English annotated for misogynistic content. Each instance is further annotated with information about the type of misogynistic behaviour (stereotype, objectification, dominance, derailing, sexual harassment, threats of violence, discredit) and target category (individual or generic target).<p> The dataset has been used in the AMI shared task  (https://amievalita2018.wordpress.com) during the Evalita 2018 evaluation campaign (http://www.evalita.it/2018). The task challenged participants to build systems that automatically identify misogynous content in Twitter.<p>In order to comply with GDPR privacy rules and Twitter’s policies, the identifiers of tweets and users have been anonymized and replaced by unique identifiers.  <p>
DOI: 10.57771/1e4t-he02
title: AMI 2020 Dataset
title: AMI 2020

description: The AMI 2020 dataset collects 6,000 tweets written in Italian, plus 3922 template-generated sentences, annotated for misogyny and aggressiveness.<p>The dataset has been used in the AMI shared task (https://amievalita2020.github.io/) during the Evalita 2020 evaluation campaign (http://www.evalita.it/2020). The task challenged participants to build systems that automatically identify misogynous content in Twitter in Italian language.<p>In order to comply with GDPR privacy rules and Twitter’s policies, the identifiers of tweets and users have been anonymized and replaced by unique identifiers.<p>
DOI: 10.57771/tw9x-kc80
title: Anaphora Resolution Dataset
title: Anaphora Resolution

description: The Anaphora Resolution Dataset collects sentences from the Wikipedia subset of the LiveMemories Anaphora Resolution Corpus (LM-WIKI, Rodrguez et al. 2010), annotated with mentions of entities according to the guidelines for the ARRAU corpus and the VENEX corpus. The dataset follows the format of the SemEval-2010 Task 1 on the Multilingual Coreference Resolution, with one token per line and an empty line after each sentence. Additional information, extracted with the TextPro toolkit and the MALT parser, is provided in tab-separated columns.<p>The dataset has been used in the context of the EVALITA 2011 Anaphora Resolution Task (http://www.evalita.it/2011/tasks/anaphora), organised as part of the EVALITA 2011 evaluation campaign (http://www.evalita.it/2011). <p>
DOI: 10.57771/qn1d-v258
title: ArtiPhon Dataset
title: ArtiPhon

description: The ArtiPhon dataset contains 666 recorded utterances selected from the Italian MSPKA corpus (http://www.mspkacorpus.it/) (Canevari et al., 2015), which was collected in 2015 at the Istituto Italiano di Tecnologia (IIT). The dataset was used as training set  in the context of the ArtiPhon task (http://www.smile.unina.it/artiphon/), organised as part of the EVALITA 2016 evaluation campaign (​​http://www.evalita.it/2016).<p>The task challenged participants to build a speaker-dependent phone recognition system evaluated on mismatched speaking rates.<p>The training dataset contains simultaneous recordings of audio and vocal tract (i.e., articulatory) movements recorded with an electromagnetic articulograph (EMA).<p>
DOI: 10.57771/4x2e-3p93
title: ATE_ABSITA dataset
title: ATE_ABSITA

description: The ATE_ABSITA dataset comprehends 4364 real user reviews, written in the Italian language, involving 23 products and collected from a famous eCommerce platform. Each sentence has been annotated by three subjects for its polarity, for the score assigned by the author, and for the presence of aspect terms. The dataset has been randomly divided into training, development and test sets. The data format used is NDJSON 1 with UTF-8 encoding and newline as delimiter. <p>The dataset has been produced and used in the context of the ATE_ABSITA task (http://www.di.uniba.it/~swap/ate_absita/index.html) during the Evalita 2020 evaluation campaign (http://www.evalita.it/2020).
DOI: 10.57771/qra3-xz85
title: Bilingual (Spanish-English) Speech synthesis HTS models
title: Modèles HTS bilingues pour la synthèse vocale (espagnol-anglais)

description: This database contains Bilingual (English and Spanish) Festival HTS models. Models were trained with 9h of speech from 2 female bilingual speakers and 2 male bilingual speakers. Each speaker recorded 2h 15 min per language.  The speech data can be found in the TC-STAR Bilingual Voice-Conversion Spanish Speech Database (ELRA-S0311) and in the TC-STAR Bilingual Expressive Spanish Speech Database (ELRA-S0313).
description: Cette base de données comprend les modèles HTS Festival bilingues (anglais et espagnol). Les modèles ont été entraînés à partir de 9 heures de parole réalisés par 2 locuteurs bilingues femmes et 2 locuteurs bilingues hommes. Chaque locuteur a enregistré 2h 15 min par langue. La base de données de parole peut être trouvée dans la base de données orale de conversion vocale bilingue TC-STAR pour l’espagnol (ELRA-S0311) et dans la base de données de parole expressive bilingue TC-STAR (ELRA-S0313).
DOI: 10.57771/rfas-b644
title: CEPLEXicon
title: CEPLEXicon

description: CEPLEXicon is a lexicon based on two different corpora of child speech – Santos corpus (Santos, 2006, Santos et al., 2014, see http://www.clul.ul.pt/resources/546?lang=en) and Freitas corpus (Freitas, 1997, Freitas et al. 2012). This lexicon results from the automatic tagging of the two corpora, using a tagger and the POS tag set produced in the research unit ANAGRAMA (Centro de Linguística da Universidade de Lisboa - CLUL) (Généreux, Hendrickx & Mendes, 2012). The automatic tagging was followed by a partial manual revision (as described in the manual).<p><p>This lexicon covers all the speech produced by seven monolingual Portuguese children aged 1;02.00 to 3;11.12, in a total of 114 files, each corresponding to 40-50 minutes of child-adult interaction in a naturalistic setting. The lexicon is presented in .xls format and includes 2201 lemmas, the number of occurrences of each lemma in three different age periods (<2 years of age; &#8805; 2 and < 3 years of age; &#8805; 3 years of age), frequency of the lemma in each period and age of first occurrence for each child.<p><p>CEPLEXicon was developed at ANAGRAMA (CLUL, Faculdade de Letras da Universidade de Lisboa), under the project Complement Clauses in the Acquisition of Portuguese (PTDC/CLE-LIN/120897/2010), funded by Fundação para a Ciência e Tecnologia.
description: CEPLEXicon est un lexique basé sur deux corpus différents de parole d’enfants – le corpus Santos (Santos, 2006, Santos et al., 2014, voir http://www.clul.ul.pt/resources/546?lang=en) et le corpus Freitas (Freitas, 1997, Freitas et al. 2012). Ce lexique résulte d’un étiquetage automatique des deux corpus, au moyen d’un étiqueteur et d’un ensemble d’étiquettes POS produits par l’unité de recherche ANAGRAMA (Centro de Linguística da Universidade de Lisboa - CLUL) (Généreux, Hendrickx & Mendes, 2012). L’étiquetage automatique a été suivi par une révision manuelle partielle (et décrite dans le manuel accompagnant le lexique).

Ce lexique couvre toute la parole produite par sept enfants portugais monolingues, âgés de 1 an et 2 mois à 3 ans, 11 mois et 12 jours, pour un total de 114 fichiers, chaque fichier correspondant à 40-50 minutes d’interaction enfant-adulte dans un contexte naturel. Le lexique est présenté au format .xls et comprend 2201 lemmes, les nombre d’occurrences de chaque lemme en trois périodes d’âges différentes (<2 ans; &#8805; 2 et < 3 ans; &#8805; 3 ans), la fréquence du lemme pour chaque période d’âge et l’âge de la première occurrence pour chaque enfant.

CEPLEXicon a été développé à ANAGRAMA (CLUL, Faculdade de Letras da Universidade de Lisboa), dans le cadre du projet “Complement Clauses in the Acquisition of Portuguese” (PTDC/CLE-LIN/120897/2010), financé par la Fundação para a Ciência e Tecnologia.
DOI: 10.57771/y2rj-vk47
title: CHANGE-IT dataset
title: CHANGE-IT

description: The CHANGE-IT dataset contains approximately 152,000 article-headline pairs, collected from two Italian newspapers situated at opposite ends of the political spectrum, namely la Repubblica (left) and Il Giornale (right), with the two newspapers equally represented. The dataset has been used in the context of the CHANGE-IT task (https://sites.google.com/view/change-it) during the Evalita 2020 evaluation campaign (http://www.evalita.it/2020). CHANGE-IT is a generation task for Italian – more specifically, a style transfer task for headlines of Italian newspapers. Given a (collection of) headlines from one newspaper, namely Il Giornale (G) or La Repubblica (R), it challenges automatic systems to change all G-headlines to headlines in style R, and all R-headlines to headlines in style G.<p>Although the task only concerns headline change, the dataset comprehends both the headlines as well as their respective full articles.<p>
DOI: 10.57771/cky7-hz44
title: CONcreTEXT - Concreteness in Context
title: CONcreTEXT

description: The CONcreTEXT dataset provides conceptual concreteness ratings for words in context. Human ratings were collected for 148 Italian and 117 English lemmas occurring in overall 1096 sentences extracted from The Human Instruction Dataset (Chocron and Pareti, 2018). The dataset was created in the context of the EVALITA 2020 evaluation campaign. More information about the CONcreTEXT task and dataset is available at the URL https://lablita.github.io/CONcreTEXT/.
DOI: 10.57771/k4t4-4609
title: Constituency Parsing Task Dataset
title: Constituency Parsing Task

description: The Constituency Parsing Task Dataset contains the dataset used for the Constituency Parsing Task (http://www.evalita.it/2009/tasks/parsing) organised as part of the EVALITA 2009 evaluation campaign (http://www.evalita.it/2009).The data for the constituency track derive from TUT, the treebank for Italian developed by the Natural Language Processing group of the Department of Computer Science of the University of Turin. The native annotation scheme of TUT is dependency-based, but in order to increase the comparability with other existing resources and in particular with the Penn Treebank, TUT has been converted in the TUT-Penn format, an adaptation of the Penn Treebank format for Italian.<p>The development set includes 2,200 sentences that correspond to 64,193 annotated tokens in TUT native format. The corpus is organized in two subcorpora, i.e. one from Italian newspaper (1,100 sentences and 33,522 tokens in TUT format) and one from the Italian Civil Law Code (1,100 sentences and 30,671 tokens in TUT format).<p>The test set includes 200 sentences and features the same balancement of the development set: 100 sentences from newspapers and 100 from the Civil Law Code. <p>
DOI: 10.57771/3mzr-6652
title: CRIPCO corpus
title: CRIPCO

description: The Cross-document Italian People Coreference (CRIPCO) corpus created at FBK is a subset of the news stories published by the local newspaper "L'Adige" from 1999 to 2006 annotated with information about person cross-document coreference. CRIPCO is composed of 43,328 documents and it was used in the NEws People Search (NePS) task (http://www.evalita.it/2011/tasks/NePS) at EVALITA 2011 (http://www.evalita.it/2011).To this purpose, the corpus was split into a development set and a test set, balanced according to the ambiguity of the Group Names and the fame of the related persons. The CRIPCO release maintains the subdivision into NePS development and test set, in order to allow comparisons with the results obtained in the evaluation campaign.<p>
DOI: 10.57771/zyhd-cn57
title: Cross-Genre Gender Prediction (GxG) dataset 
title: GxG dataset

description: The GxG dataset collects 21874 documents from five different genres (Twitter, YouTube, children writing, news/journalism, personal diaries) annotated with the gender of the author. The dataset has been used in the Cross-Genre Gender Prediction (GxG) task (https://sites.google.com/view/gxg2018), a shared task on author profiling (in terms of gender) on Italian texts, with a specific focus on cross-genre performance, organised as part of EVALITA 2018 (http://www.evalita.it/2018). <p>The dataset is divided into training and test data, constituted of respectively 454585 and 374012 tokens . The distribution of gender labels is controlled for in each dataset (50/50). In order to comply with GDPR privacy rules and Twitter’s policies, the identifiers of tweets and users have been anonymized and replaced by unique identifiers.
DOI: 10.57771/9e7r-cc52
title: DADOEVAL corpus
title: DADOEVAL

description: The DADOEVAL corpus consists of 2,759 documents written by the Italian politician Alcide De Gasperi, manually tagged with the date of issue (comprehended between 1901 and 1954). It contains De Gasperi’s public documents and 100 private letters.<p>The dataset has been used in the context of the DADOEVAL task (https://dhfbk.github.io/DaDoEval/) during the Evalita 2020 evaluation campaign (http://www.evalita.it/2020). The task challenged participants to build systems that automatically assign temporal information to documents written in Italian. The evaluation exercise comprises three levels of temporal granularity, from coarse-grained to year-based, and includes two types of test sets, either having the same genre of the training set, or a different one.
DOI: 10.57771/1t35-g168
title: DankMemes Dataset
title: DankMemes

description: The DANKMEMES  Dataset is composed of 2,361 images,  half memes and half not, automatically extracted from Instagram through a Python script aimed at the hashtag related to the Italian government crisis (“#crisidigoverno”). It was created and used in the context of the DankMemes (https://dankmemes2020.fileli.unipi.it), a shared task proposed for the 2020 EVALITA campaign (http://www.evalita.it/2020), focusing on the automatic classification of In- ternet memes. The task encompasses three subtasks, aimed at: detecting memes (Task A), detecting the hate speech in memes (Task B) and clustering memes according to events (Task C).<p>The dataset is split into training and test sets, in a proportion of 80-20% of items. The test dataset has been provided without gold labels, provided in a separate file for each subtask.<p><p>For each subtask, the dataset consists of:<p>a folder with images in .jpg format <p>- a .csv file with the associated image embeddigs, computed employing ResNet (He et al., 2016), a state-of-the-art model for image recognition based on Deep Residual Learning.<p>- a .csv file with the associated variables<p><p><p>The variables provided are:<p><p>- File: the name of the image file associated with the variables;<p><p>- Engagement: the number of comments and likes of the image;<p><p>- Date: when the image has first been posted on Instagram;<p><p>- Picture manipulation: entails the degree of visual modification of the images. Non-manipulated or low impact changes are labeled 0 (e.g. addition of text, or logo). Heavily manipulated, impactful changes (e.g. images altered to include political actors) are labeled 1;<p><p>- Visual actors: the political actors (i.e. politicians, parties’ logos) portrayed visually, as edited into the picture or portrayed in the original image;<p><p>- Text: the textual content of the image has been extracted through optical character recognition (OCR) using Google’s Tesseract-OCR Engine, and further manually corrected;<p><p>- (for task A) Meme: binary feature, where 0 represents non meme images and 1 meme images. <p><p>- (for task B) Hate speech: binary feature only for memes. It differentiates memes with offensive language (1) from non offensive memes (0).<p><p>- (for task C) Event: feature only for meme images, categorizing them according to 4 events related to the 2019 Italian government crisis<p>
DOI: 10.57771/wbgn-wm20
title: DankMemes Task A Dataset
title: DankMemes Task A

description: The DANKMEMES Task A Dataset is comprised of 2,000 images,  half memes and half not, automatically extracted from Instagram through a Python script aimed at the hashtag related to the Italian government crisis (“#crisidigoverno”). It was created and used in the context of the DankMemes (https://dankmemes2020.fileli.unipi.it), a shared task proposed for the 2020 EVALITA campaign (http://www.evalita.it/2020), focusing on the automatic classification of Internet memes.<p>The dataset is split into training and test sets, in a proportion of 80-20% of items. The test dataset has been provided without gold labels, i.e. without the “Meme” attribute; the gold labels are provided in a separate file.<p>The dataset consists of:<p>- a folder with images in .jpg format <p>- a .csv file with the associated image embeddigs, computed employing ResNet (He et al., 2016), a state-of-the-art model for image recognition based on Deep Residual Learning<p>- a .csv file with the associated variables.<p>The variables provided for this task are:<p><p>- File: the name of the image file associated with the variables;<p>- Engagement: the number of comments and likes of the image;<p>- Date: when the image has first been posted on Instagram;<p>- Picture manipulation: entails the degree of visual modification of the images. Non-manipulated or low impact changes are labeled 0 (e.g. addition of text, or logo). Heavily manipulated, impactful changes (e.g. images altered to include political actors) are labeled 1;<p>- Visual actors: the political actors (i.e. politicians, parties’ logos) portrayed visually, as edited into the picture or portrayed in the original image;<p>- Text: the textual content of the image has been extracted through optical character recognition (OCR) using Google’s Tesseract-OCR Engine, and further manually corrected;<p>- Meme: binary feature, where 0 represents non meme images and 1 meme images. This is the target label for the first subtask.<p>
DOI: 10.57771/d40t-5456
title: DankMemes Task B Dataset
title: DankMemes Task B

description: The DANKMEMES Task B Dataset collects 1,000 images,  half memes and half not, automatically extracted from Instagram through a Python script aimed at the hashtag related to the Italian government crisis (“#crisidigoverno”). It was created and used in the context of the DankMemes (https://dankmemes2020.fileli.unipi.it), a shared task proposed for the 2020 EVALITA campaign (http://www.evalita.it/2020), focusing on the automatic classification of Internet memes.<p>The dataset is split into training and test sets, in a proportion of 80-20% of items. The test dataset has been provided without gold labels, provided in a separate file.<p>The dataset consists of:<p>- a folder with images in .jpg format <p>- a .csv file with the associated image embeddigs, computed employing ResNet (He et al., 2016), a state-of-the-art model for image recognition based on Deep Residual Learning<p>- a .csv file with the associated variables.<p>The variables provided for this task are:<p><p>- File: the name of the image file associated with the variables;<p>- Engagement: the number of comments and likes of the image;<p>- Date: when the image has first been posted on Instagram;<p>- Picture manipulation: entails the degree of visual modification of the images. Non-manipulated or low impact changes are labeled 0 (e.g. addition of text, or logo). Heavily manipulated, impactful changes (e.g. images altered to include political actors) are labeled 1;<p>- Visual actors: the political actors (i.e. politicians, parties’ logos) portrayed visually, as edited into the picture or portrayed in the original image;<p>- Text: the textual content of the image has been extracted through optical character recognition (OCR) using Google’s Tesseract-OCR Engine, and further manually corrected;<p>- Hate speech: binary feature only for memes. It differentiates memes with offensive language (1) from non offensive memes (0). This is the target label for the second subtask.<p>
DOI: 10.57771/d3a5-jy71
title: DankMemes Task C Dataset
title: DankMemes Task C

description: The DANKMEMES Task C Dataset consists of 1,000 images,  half memes and half not, automatically extracted from Instagram through a Python script aimed at the hashtag related to the Italian government crisis (“#crisidigoverno”). It was created and used in the context of the DankMemes (https://dankmemes2020.fileli.unipi.it), a shared task proposed for the 2020 EVALITA campaign (http://www.evalita.it/2020), focusing on the automatic classification of Internet memes.<p>The dataset is split into training and test sets, in a proportion of 80-20% of items. The test dataset has been provided without gold labels, provided in a separate file.<p>The dataset consists of:<p>- a folder with images in .jpg format <p>- a .csv file with the associated image embeddigs, computed employing ResNet (He et al., 2016), a state-of-the-art model for image recognition based on Deep Residual Learning<p>- a .csv file with the associated variables.<p>The variables provided for this task are:<p><p>- File: the name of the image file associated with the variables;<p>- Engagement: the number of comments and likes of the image;<p>- Date: when the image has first been posted on Instagram;<p>- Picture manipulation: entails the degree of visual modification of the images. Non-manipulated or low impact changes are labeled 0 (e.g. addition of text, or logo). Heavily manipulated, impactful changes (e.g. images altered to include political actors) are labeled 1;<p>- Visual actors: the political actors (i.e. politicians, parties’ logos) portrayed visually, as edited into the picture or portrayed in the original image;<p>- Text: the textual content of the image has been extracted through optical character recognition (OCR) using Google’s Tesseract-OCR Engine, and further manually corrected;<p>- Event: feature only for meme images, categorizing them according to 4 events related to the 2019 Italian government crisis. <p>