In this paper we present designated solutions for sentiment classification and sarcasm detection tasks that were introduced as part of a shared task by Abu Farha et al. (2021).
Natural language processing tools have been shown to be effective for detecting symptoms of schizophrenia in transcribed speech.
This model has been considered as a method of describing genetic relationships between languages.
We observe a recent behaviour on social media, in which users intentionally remove consonantal dots from Arabic letters, in order to bypass content-classification algorithms.
To measure the contribution of context to learning, we also tested word-shuffled data, for which the error rises to 2. 5%.
Natural language processing tools are used to automatically detect disturbances in transcribed speech of schizophrenia inpatients who speak Hebrew.