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.
Painter Alfons Mucha at Zbiroh Chateau working on The Battle of Grünwald from his Slav Epic cycle. Mucha in his studio working on a design for the windows of St. Vitus Cathedral. Mucha in the garden of his villa in Prague-Bubeneč. Mucha with his wife Marie (née Chytilová), son Jiří, and daughter Jaroslava. Mucha with painters Max Švabinský and Alois Kalvoda.