Diego Reforgiato Recupero
Cited by
Cited by
Sentiment analysis: Adjectives and adverbs are better than adjectives alone.
F Benamara, C Cesarano, A Picariello, DR Recupero, VS Subrahmanian
ICWSM 7, 203-206, 2007
Semantic web machine reading with FRED
A Gangemi, V Presutti, D Reforgiato Recupero, AG Nuzzolese, ...
Semantic Web 8 (6), 873-893, 2017
AVA: Adjective-verb-adverb combinations for sentiment analysis
VS Subrahmanian, D Reforgiato
IEEE Intelligent Systems 23 (4), 43-50, 2008
Deep learning and time series-to-image encoding for financial forecasting
S Barra, SM Carta, A Corriga, AS Podda, DR Recupero
IEEE/CAA Journal of Automatica Sinica 7 (3), 683-692, 2020
Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting
S Carta, A Ferreira, AS Podda, DR Recupero, A Sanna
Expert systems with applications 164, 113820, 2021
Annotated rdf
O Udrea, DR Recupero, VS Subrahmanian
ACM Transactions on Computational Logic (TOCL) 11 (2), 1-41, 2010
Frame-based detection of opinion holders and topics: a model and a tool
A Gangemi, V Presutti, DR Recupero
IEEE Computational Intelligence Magazine 9 (1), 20-30, 2014
Sentilo: frame-based sentiment analysis
D Reforgiato Recupero, V Presutti, S Consoli, A Gangemi, AG Nuzzolese
Cognitive Computation 7, 211-225, 2015
A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning
S Carta, A Corriga, A Ferreira, AS Podda, DR Recupero
Applied Intelligence 51, 889-905, 2021
Framester: A wide coverage linguistic linked data hub
A Gangemi, M Alam, L Asprino, V Presutti, DR Recupero
Knowledge Engineering and Knowledge Management: 20th International …, 2016
Bridging learning analytics and cognitive computing for big data classification in micro-learning video collections
D Dessì, G Fenu, M Marras, DR Recupero
Computers in Human Behavior 92, 468-477, 2019
Fraud detection for E-commerce transactions by employing a prudential Multiple Consensus model
S Carta, G Fenu, DR Recupero, R Saia
Journal of Information Security and Applications 46, 13-22, 2019
A new unsupervised method for document clustering by using WordNet lexical and conceptual relations
D Reforgiato Recupero
Information Retrieval 10, 563-579, 2007
Human-centric artificial intelligence architecture for industry 5.0 applications
JM Rožanec, I Novalija, P Zajec, K Kenda, H Tavakoli Ghinani, S Suh, ...
International journal of production research 61 (20), 6847-6872, 2023
System and method for analysis of an opinion expressed in documents with regard to a particular topic
VS Subrahmanian, DR Reforgiato, A Picariello, BJ Dorr, C Cesarano, ...
US Patent 8,296,168, 2012
Generating knowledge graphs by employing natural language processing and machine learning techniques within the scholarly domain
D Dessì, F Osborne, DR Recupero, D Buscaldi, E Motta
Future Generation Computer Systems 116, 253-264, 2021
Antipole tree indexing to support range search and k-nearest neighbor search in metric spaces
D Cantone, A Ferro, A Pulvirenti, DR Recupero, D Shasha
IEEE transactions on knowledge and data engineering 17 (4), 535-550, 2005
Ensembling classical machine learning and deep learning approaches for morbidity identification from clinical notes
V Kumar, DR Recupero, D Riboni, R Helaoui
IEEE Access 9, 7107-7126, 2020
AI-KG: an automatically generated knowledge graph of artificial intelligence
D Dessì, F Osborne, D Reforgiato Recupero, D Buscaldi, E Motta, H Sack
The Semantic Web–ISWC 2020: 19th International Semantic Web Conference …, 2020
Explainable machine learning exploiting news and domain-specific lexicon for stock market forecasting
SM Carta, S Consoli, L Piras, AS Podda, DR Recupero
IEEE Access 9, 30193-30205, 2021
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