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From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning
But randomly sampling data to be annotated is often inefficient as it ignores the characteristics of the data and the specific needs of the model.
ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modeling
Traditional text classification approaches often require a good amount of labeled data, which is difficult to obtain, especially in restricted domains or less widespread languages.
DEBACER: a method for slicing moderated debates
In this paper, we (i) propose a new algorithm, DEBACER, which partitions moderated debates; (ii) carry out a comparative study between conventional and BERTimbau pipelines; and (iii) validate DEBACER applying it to the minutes of the Assembly of the Republic of Portugal.
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