no code implementations • SMM4H (COLING) 2020 • Silvia Casola, Alberto Lavelli
This paper describes a classifier for tweets that mention medications or supplements, based on a pretrained transformer.
no code implementations • 11 Apr 2024 • Iker García-Ferrero, Rodrigo Agerri, Aitziber Atutxa Salazar, Elena Cabrio, Iker de la Iglesia, Alberto Lavelli, Bernardo Magnini, Benjamin Molinet, Johana Ramirez-Romero, German Rigau, Jose Maria Villa-Gonzalez, Serena Villata, Andrea Zaninello
While these LLMs display competitive performance on automated medical texts benchmarks, they have been pre-trained and evaluated with a focus on a single language (English mostly).
1 code implementation • 24 Oct 2023 • Silvia Casola, Alberto Lavelli, Horacio Saggion
Patents are legal documents that aim at protecting inventions on the one hand and at making technical knowledge circulate on the other.
no code implementations • 30 Apr 2021 • Silvia Casola, Alberto Lavelli
We survey Natural Language Processing (NLP) approaches to summarizing, simplifying, and generating patents' text.
no code implementations • LREC 2020 • Bernardo Magnini, Alberto Lavelli, Simone Magnolini
We present a comparison between deep learning and traditional machine learning methods for various NLP tasks in Italian.
no code implementations • 15 Aug 2019 • Seyedmostafa Sheikhalishahi, Riccardo Miotto, Joel T. Dudley, Alberto Lavelli, Fabio Rinaldi, Venet Osmani
There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods.
no code implementations • LREC 2012 • Ramona Bongelli, Carla Canestrari, Ilaria Riccioni, Andrzej Zuczkowski, Cinzia Buldorini, Ricardo Pietrobon, Alberto Lavelli, Bernardo Magnini
Uncertainty language permeates biomedical research and is fundamental for the computer interpretation of unstructured text.
no code implementations • LREC 2012 • Anita Alicante, Cristina Bosco, Anna Corazza, Alberto Lavelli
The aim of this paper is to contribute to the debate on the issues raised by Morphologically Rich Languages, and more precisely to investigate, in a cross-paradigm perspective, the influence of the constituent order on the data-driven parsing of one of such languages(i. e. Italian).
no code implementations • LREC 2012 • Md. Faisal Mahbub Chowdhury, Alberto Lavelli
Relation extraction (RE) is an important text mining task which is the basis for further complex and advanced tasks.