Daniele Sartiano


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Linear Neural Parsing and Hybrid Enhancement for Enhanced Universal Dependencies
Giuseppe Attardi | Daniele Sartiano | Maria Simi
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

To accomplish the shared task on dependency parsing we explore the use of a linear transition-based neural dependency parser as well as a combination of three of them by means of a linear tree combination algorithm. We train separate models for each language on the shared task data. We compare our base parser with two biaffine parsers and also present an ensemble combination of all five parsers, which achieves an average UAS 1.88 point lower than the top official submission. For producing the enhanced dependencies, we exploit a hybrid approach, coupling an algorithmic graph transformation of the dependency tree with predictions made by a multitask machine learning model.


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UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification
Giuseppe Attardi | Daniele Sartiano
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


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UniPi: Recognition of Mentions of Disorders in Clinical Text
Giuseppe Attardi | Vittoria Cozza | Daniele Sartiano
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)