12 research outputs found
An experimental annotation task to investigate annotators’ subjectivity in a misogyny dataset
Recognizing Hate with NLP: The Teaching Experience of the #DeactivHate Lab in Italian High Schools
WikiBio: a Semantic Resource for the Intersectional Analysis of Biographical Events
Biographical event detection is a relevant task for the exploration and comparison of the ways in which people's lives are told and represented. In this sense, it may support several applications in digital humanities and in works aimed at exploring bias about minoritized groups. Despite that, there are no corpora and models specifically designed for this task. In this paper we fill this gap by presenting a new corpus annotated for biographical event detection. The corpus, which includes 20 Wikipedia biographies, was compared with five existing corpora to train a model for the biographical event detection task. The model was able to detect all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an F-score of 0.859. Finally, the model was used for performing an analysis of biases about women and non-Western people in Wikipedia biographies.</p
EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020
Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)
APPReddit: a Corpus of Reddit Posts Annotated for Appraisal
Despite the large number of computational resources for emotion recognition,
there is a lack of data sets relying on appraisal models. According to
Appraisal theories, emotions are the outcome of a multi-dimensional evaluation
of events. In this paper, we present APPReddit, the first corpus of
non-experimental data annotated according to this theory. After describing its
development, we compare our resource with enISEAR, a corpus of events created
in an experimental setting and annotated for appraisal. Results show that the
two corpora can be mapped notwithstanding different typologies of data and
annotations schemes. A SVM model trained on APPReddit predicts four appraisal
dimensions without significant loss. Merging both corpora in a single training
set increases the prediction of 3 out of 4 dimensions. Such findings pave the
way to a better performing classification model for appraisal prediction