M
Marco Pota
Researcher at Indian Council of Agricultural Research
Publications - 40
Citations - 527
Marco Pota is an academic researcher from Indian Council of Agricultural Research. The author has contributed to research in topics: Fuzzy logic & Fuzzy classification. The author has an hindex of 9, co-authored 33 publications receiving 318 citations. Previous affiliations of Marco Pota include National Research Council.
Papers
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Journal ArticleDOI
An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian.
TL;DR: In this paper, the authors proposed a different approach for Twitter sentiment analysis based on two steps: first, the tweet jargon, including emojis and emoticons, is transformed into plain text, exploiting procedures that are languageindependent or easily applicable to different languages.
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Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification
Marco Pota,Elisa Scalco,Giuseppe Sanguineti,Alessia Farneti,Giovanni Mauro Cattaneo,Giovanna Rizzo,Massimo Esposito +6 more
TL;DR: This paper proposes the application of a novel machine learning approach, based on both statistics and fuzzy logic, aimed at the classification of patients at risk of parotid gland shrinkage and 12-months xerostomia, with the aim of individuating predictors and models to classify respective outcomes.
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Multilingual evaluation of pre-processing for BERT-based sentiment analysis of tweets
TL;DR: Results allow to individuate the most convenient strategy to pre-process tweets, and thus to improve the state of the art in both languages for the considered task of sentiment analysis.
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Multilingual POS tagging by a composite deep architecture based on character-level features and on-the-fly enriched Word Embeddings
TL;DR: A POS tagging system based on a deep neural network made of a static and task-independent pre-trained model for representing words semantics enriched by morphological information, by approximating the Word Embedding representation learned from an unlabelled corpus by the fastText model is proposed.
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Designing rule-based fuzzy systems for classification in medicine
TL;DR: A general procedure for constructing rule-based fuzzy classifiers, according to the system characteristics of performance and interpretability required by the specific application, which can be used with any type of data, and is particularly useful for the medical field requirements.