Improving Amazigh POS tagging using machine learning
Tamazight, Berber, and Amazigh are the multiple names for the same language.
Tamazight, Berber, and Amazigh are the multiple names for the same language.
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.
During the research, an information system for voicing Ukrainian-language text was developed based on NLP and machine learning methods. The created information system is implemented in the form of a desktop application, which allows the process of voicing the Ukrainian-language text. The created system included all stages of software development: the design process, the implementation process, and the testing process.
The article presents an overview of computational resources for the Ukrainian language within a multilingual European MULTEXT-East project (MTE, http://nl.ijs.si/ME/V4) freely available for researchers since May 2010, including a formal representation of morphosyntactic specifications consisting of 1239 unique grammatical tags in the XML, TEI-5 compatible, format and a morphosyntactic lexicon covering over 200000 wordforms with lemmas and morphosyntactic codes.