NLP

Sentiment analysis is an essential technique for classifying and extracting emotions from several data sets.  While many basic methods distinguish between negative and positive emotions, advanced approaches may consider additional categories, such as neutral emotions.  This becomes very important and difficult when we need to deal with less parsed languages and dialects, such as Moroccan Darija.  Our study highlights the nuances of conducting sentiment analysis implementing the MAC dataset, which includes comments in Moroccan Darija.  Our main target is to do comparativ

A method for data set formation has been developed to verify the ability of pre-trained models to learn transitivity dependencies. The generated data set was used to test the quality of learning the transitivity dependencies in the task of natural language inference (NLI). Testing of a data set with a size of 10,000 samples (MultiNLI) used to test the RoBerta model.