cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57771/j8ah-m778
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/ytbx-vm38
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/tffa-kt85
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/np8f-s474
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/vaka-4x82
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/h9aa-dk78
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/ncty-s360
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/v5ep-dq22
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/drd7-9d58
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/ps17-a456
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>
DOI: 10.57771/sk83-hc27
title: deL1L2IM corpus
title: Corpus deL1L2IM

description: The deL1L2IM corpus, created between May and August 2012 and last updated in August 2014, has been collected within the framework of a PhD project on the development of a learning method implying conversations with an artificial companion. This PhD work is presented as a qualitative investigation of instant messaging dialogues on a long-term basis (four months) between advanced learners of German and German native speakers, chatting about whatever topic they wish.<p><p>The dataset is composed of 72 dialogues, each of them having a duration of 20 to 45 minutes. The whole corpus contains ca. 52,000 words and 4,800 messages and has a file size of 0.5 Mb. Nine pairs of participants – i.e. nine learners and four native speakers – were required, with 8 dialogues per pair.<p><p>The interactions have undergone linguistic analysis whereby the annotation will be performed only on repair/correction sequences (incomplete learner error annotation). The goal of the project was to create an application for language modelling and to improve learner language applications, tutoring software and dialogue systems.<p><p>The corpus is delivered in one written text file (in XML format, customized under TEI P5).
description: Le corpus deL1L2IM, créé entre mai et août 2012, avec une dernière mise à jour en août 2014, a été collecté dans le cadre d’un projet de thèse sur le développement d’une méthode d’apprentissage impliquant des conversations avec un compagnon artificiel. Le travail de thèse se présente comme une investigation qualitative sur les dialogues de messagerie instantanée sur une période à long terme (quatre mois) entre des apprenants avancés de l’allemand et des locuteurs natifs allemands, qui ont tchatté sur n’importe quel sujet souhaité.

La base de données est composée de 72 dialogues, chacun ayant une durée de 20 à 45 minutes. Le corpus entier comprend env. 52,000 mots et 4,800 messages, pour une taille de fichier de 0,5 Mo. Neuf paires de participants ont été requises, neuf apprenants et neuf locuteurs natifs, pour réaliser 8 dialogues par paire.

Les interactions ont été soumises à une analyse linguistique où une annotation a été réalisée uniquement sur des séquences de réparation-correction (annotation des erreurs de l’apprenant incomplète). Le but du projet était de permettre la création d’une application pour la modélisation de la langue et d’améliorer les applications d’apprentissage de la langue, d’outils de tutorat et de systèmes de dialogues.

Le corpus est fourni en un fichier texte (au format XML, customisé sous TEI P5).
DOI: 10.57771/fr18-fa51
title: Dependency Parsing Dataset
title: DP

description: The Dependency Parsing Dataset contains texts taken from the ISDT (Italian Stanford Dependency Treebank) for a total of 178553 tokens, split into a training set (165,975 tokens) and a validation set (12,578 tokens). The resource is annotated according to the Stanford dependencies scheme and complies with the standard CoNLL-X format. The data set has been used in the context of the Dependency Parsing Task (http://medialab.di.unipi.it/Evalita2014/), organised as part of the EVALITA 2014 evaluation campaign (​​http://www.evalita.it/2014).
DOI: 10.57771/cj7v-sr71
title: Dependency Parsing Task Dataset
title: Dependency Parsing Task

description: The Dependency Parsing Task Dataset contains the dataset used for the Main Dependency Parsing Task (http://www.evalita.it/2009/tasks/parsing) organised as part of the EVALITA 2009 evaluation campaign (http://www.evalita.it/2009). It comprehends data from TUT, the treebank for Italian developed by the Natural Language Processing group of the Department of Computer Science of the University of Turin.<p>The training set includes 2,400 sentences that correspond to 72,149 annotated tokens in TUT native format, and 66,055 tokens in CoNLL format. The corpus can be separated in three subcorpora, i.e. one from Italian newspapers (1,100 sentences and 30,561 tokens in CoNLL format), one from the Italian Civil Law Code (1,100 sentences and 28,048 tokens), and one from the Italian section of the JRC-Acquis Multilingual Parallel Corpus, a collection of declarations of the European Community (200 sentences and 7,446 tokens).<p>The test set of the MDS includes 240 sentences (5,287 tokens) and features a balancement alike to that of the training set: 100 sentences (1,782 tokens) from newspapers, 100 sentences (2,293 tokens) from Civil Law Code and 40 sentences (1,212 tokens) from the Passage/JRC-Acquis corpus.
DOI: 10.57771/ynxg-hb22
title: DIACR-Ita dataset
title: DIACR-Ita

description: The DIACR-Ita dataset comprehends the test set used for the DIACR-Ita shared task (https://diacr-ita.github.io/DIACR-Ita/), organised as part of Evalita 2020 evaluation campaign (http://www.evalita.it/2020). The task challenged participants to develop systems that can automatically detect if a given word has changed its meaning over time, given contextual information from corpora.<p>For the test phase, the following data were provided:<p>1- a diachronic split of the “L’Unita`” corpus into the two sub-corpora, C1 and C2, each belonging to a specific time period (respectively, 1945 − 1970 and 1990 − 2014);<p>2- 18 target words, among which 6 were identified as target of semantic meaning change between the two time periods.<p>The present item refers to the 18 target words and their gold labels.<p>
DOI: 10.57771/e0rw-h354
title: English Annotated Dictionary with Morphological and Linguistic Features
title: English Annotated Dictionary

description: The English Annotated Dictionary  consists of i) a limited size dictionary of English words and ii) a language model for the English language. The annotated dictionary provides useful information about words including chunking and syllabification as well as   grammatical information  such as “parts of speech”. The language model captures the journey of a student learning to read. It consists of a collection of language features that must be mastered by a reader. For each language feature, the model specifies its difficulty as well as a list of prerequisites that are typically mastered before the new feature is introduced in the learning path. What makes iRead English  Annotated Dictionary special is the information associating each dictionary lemma with features in the language model. The iRead team of linguists and education researchers used rigorous research techniques informed by the latest science to develop the provided resources.
DOI: 10.57771/55r9-w098
title: English Child Dictionary
title: English Child Dictionary

description: The English Child Dictionary is a limited size dictionary of child-appropriate English words. It provides information about words including part of speech, phonetic transcription, syllabification and grapheme-phoneme correspondence. Additional information on prefixes and suffixes is included. The iRead team of linguists and education researchers used rigorous research techniques informed by the latest research to develop the provided resources.
DOI: 10.57771/hntn-rx89
title: English-Nepali Parallel Corpus
title: Corpus parallèle anglais-népalais

description: The Nepali Monolingual written corpus is one of the 3 resources that constitute the Nepali National Corpus. The Nepali National Corpus was produced in 2006 in the framework of the project Bhasha Sanchar (“language communication”), also known as Nelralec, for Nepali Language Resources and Localization for Education and Communication; funded by the EU Asia IT&C programme, reference number ASIE/2004/091-777.<p><p>This corpus consists of a collection of national development texts in English and Nepali and is divided as follows:<p>- a small set of data aligned at the sentence level (27,060 English words; 21,756 Nepali words), provided in the tmx format (xml file):<p>- a larger set of texts aligned at the document level (617,340 English words; 596,571 Nepali words), provided in raw text and in the original word processing format.<p>- an additional set of monolingual data in Nepali (386,879 words in Nepali), provided in raw text and in the original word processing format.
description: Le corpus écrit monolingue népalais est l’une des 3 ressources qui composent le Corpus National Népalais. Le Corpus National Népalais a été produit en 2006 dans le cadre du projet Bhasha Sanchar (“communication langagière”), également connu sous le nom de Nelralec (Ressources linguistiques népalaises et localisation pour l’éducation et la communication); il a été financé par le programme de l’Union Européenne Asia IT&C, sous la référence ASIE/2004/091-777.

Ce corpus consiste en une collection de textes dans le domaine du développement national en anglais et népalais et est réparti comme suit:
- un petit ensemble de données alignées au niveau de la phrase (27 060 mots anglais; 21 756 mots népalais), fourni au format tmx (fichier xml):
- un plus grand ensemble de textes alignés au niveau du document (617 340 mots anglais; 596 571 mots népalais), fourni en texte brut dans le format de traitement word d’origine.
- un ensemble additionnel de données monolingues en népalais (386 879 mots en népalais), fournis en texte brut et dans le format de traitement word d’origine.