AlbMoRe is a sentiment analysis corpus of movie reviews in Albanian, consisting of 800 records in CSV format. Each record includes a text review retrieved from IMDb and translated in Albanian by the author. It also contains a 0 negative) or 1 (positive) label added by the author. The corpus is fully balanced, consisting of 400 positive and 400 negative reviews about 67 movies of different genres. AlbMoRe corpus is released under CC-BY license (https://creativecommons.org/licenses/by/4.0/). If using the data, please cite the following paper: Çano Erion. AlbMoRe: A Corpus of Movie Reviews for Sentiment Analysis in Albanian. CoRR, abs/2306.08526, 2023. URL https://arxiv.org/abs/2306.08526.
AlbNER is a Named Entity Recognition corpus of Wikipedia sentences in Albanian, consisting of 900 records. The sentence tokens are manually labeled complying with the CoNLL-2003 shared task annotation scheme explained at https://aclanthology.org/W03-0419.pdf that uses I-ORG, B-ORG, I-PER, B-PER, I-LOC, B-LOC, I-MISC, B-MISC and O tags. AlbNER data are released under CC-BY license (https://creativecommons.org/licenses/by/4.0/). If using AlbMoRe corpus, please cite the following paper: Çano Erion. AlbNER: A Corpus for Named Entity Recognition in Albanian. CoRR, abs/2309.08741, 2023. URL https://arxiv.org/abs/2309.08741.
AlbNews is a topic modeling corpus of news headlines in Albanian, consisting of 600 labeled samples and 2600 unlabeled samples. Each labeled sample includes a headline text retrieved from Albanian online news portals. It also contains one of the four labels: 'pol' for politics, 'cul' for culture, 'eco' for economy, and 'spo' for sport. Each of the unlabeled samples contain a headline text only.AlbTopic corpus is released under CC-BY 4.0 license (https://creativecommons.org/licenses/by/4.0/). If using the data, please cite the following paper:
Çano Erion, Lamaj Dario. AlbNews: A Corpus of Headlines for Topic Modeling in Albanian. CoRR, abs/2402.04028, 2024. URL: https://arxiv.org/abs/2402.04028.
A dataset intended for fully trainable natural language generation (NLG) systems in task-oriented spoken dialogue systems (SDS), covering the English public transport information domain. It includes preceding context (user utterance) along with each data instance (pair of source meaning representation and target natural language paraphrase to be generated).
Taking the form of the previous user utterance into account for generating the system response allows NLG systems trained on this dataset to entrain (adapt) to the preceding utterance, i.e., reuse wording and syntactic structure. This should presumably improve the perceived naturalness of the output, and may even lead to a higher task success rate.
Crowdsourcing has been used to obtain natural context user utterances as well as natural system responses to be generated.
Annotate is a web and desktop application that should simplify the process of transforming photos of manuscripts to a browsable collection. It also allows users to annotate parts of the displayed images.
The PARSEME shared task aims at identifying verbal MWEs in running texts. Verbal MWEs include idioms (let the cat out of the bag), light verb constructions (make a decision), verb-particle constructions (give up), and inherently reflexive verbs (se suicider 'to suicide' in French). VMWEs were annotated according to the universal guidelines in 18 languages. The corpora are provided in the parsemetsv format, inspired by the CONLL-U format.
For most languages, paired files in the CONLL-U format - not necessarily using UD tagsets - containing parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe).
This item contains training and test data, tools and the universal guidelines file.
This multilingual resource contains corpora in which verbal MWEs have been manually annotated. VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). VMWEs were annotated according to the universal guidelines in 19 languages. The corpora are provided in the cupt format, inspired by the CONLL-U format. The corpora were used in the 1.1 edition of the PARSEME Shared Task (2018).
For most languages, morphological and syntactic information – not necessarily using UD tagsets – including parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe).
This item contains training, development and test data, as well as the evaluation tools used in the PARSEME Shared Task 1.1 (2018).
The annotation guidelines are available online: http://parsemefr.lif.univ-mrs.fr/parseme-st-guidelines/1.1
This multilingual resource contains corpora in which verbal MWEs have been manually annotated, gathered at the occasion of the 1.2 edition of the PARSEME Shared Task on semi-supervised Identification of Verbal MWEs (2020).
VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do).
For the 1.2 shared task edition, the data covers 14 languages, for which VMWEs were annotated according to the universal guidelines. The corpora are provided in the cupt format, inspired by the CONLL-U format.
Morphological and syntactic information – not necessarily using UD tagsets – including parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe).
This item contains training, development and test data, as well as the evaluation tools used in the PARSEME Shared Task 1.2 (2020). The annotation guidelines are available online: http://parsemefr.lif.univ-mrs.fr/parseme-st-guidelines/1.2
Annotated corpus of 350 decision of Czech top-tier courts (Supreme Court, Supreme Administrative Court, Constitutional Court).
280 decisions were annotated by one trained annotator and then manually adjudicated by one trained curator. 70 decisions were annotated by two trained annotators and then manually adjudicated by one trained curator. Adjudication was conducted destructively, therefore dataset contains only the correct annotations and does not contain all original annotations.
Corpus was developed as training and testing material for text segmentation tasks. Dataset contains decision segmented into Header, Procedural History, Submission/Rejoinder, Court Argumentation, Footer, Footnotes, and Dissenting Opinion. Segmentation allows to treat different parts of text differently even if it contains similar linguistic or other features.
Human post-edited test sentences for the WMT 2017 Automatic post-editing task. This consists in 2,000 English sentences belonging to the IT domain and already tokenized. Source and target segments can be downloaded from: https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-2132. All data is provided by the EU project QT21 (http://www.qt21.eu/).