no code implementations • ACL (LChange) 2021 • Pierpaolo Basile, Annalina Caputo, Tommaso Caselli, Pierluigi Cassotti, Rossella Varvara
The use of automatic methods for the study of lexical semantic change (LSC) has led to the creation of evaluation benchmarks.
no code implementations • 11 May 2024 • Marco Polignano, Pierpaolo Basile, Giovanni Semeraro
We fine-tuned the original 8B parameters instruction tuned model using the Supervised Fine-tuning (SFT) technique on the English and Italian language datasets in order to improve the original performance.
no code implementations • 15 Dec 2023 • Pierpaolo Basile, Elio Musacchio, Marco Polignano, Lucia Siciliani, Giuseppe Fiameni, Giovanni Semeraro
By leveraging an open science philosophy, this study contributes to Language Adaptation strategies for the Italian language by introducing the novel LLaMAntino family of Italian LLMs.
1 code implementation • 2 Jul 2021 • Adam Tsakalidis, Pierpaolo Basile, Marya Bazzi, Mihai Cucuringu, Barbara McGillivray
Lexical semantic change (detecting shifts in the meaning and usage of words) is an important task for social and cultural studies as well as for Natural Language Processing applications.
no code implementations • SEMEVAL 2020 • Pierluigi Cassotti, Annalina Caputo, Marco Polignano, Pierpaolo Basile
This paper describes the system proposed for the SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection.
no code implementations • RANLP 2019 • Pierpaolo Basile, Annalina Caputo, Seamus Lawless, Giovanni Semeraro
In the last few years, the increasing availability of large corpora spanning several time periods has opened new opportunities for the diachronic analysis of language.
no code implementations • RANLP 2019 • Adam Tsakalidis, Marya Bazzi, Mihai Cucuringu, Pierpaolo Basile, Barbara McGillivray
Semantic change detection (i. e., identifying words whose meaning has changed over time) started emerging as a growing area of research over the past decade, with important downstream applications in natural language processing, historical linguistics and computational social science.
2 code implementations • WS 2017 • Gaetano Rossiello, Pierpaolo Basile, Giovanni Semeraro
The textual similarity is a crucial aspect for many extractive text summarization methods.
no code implementations • 8 Feb 2017 • Claudio Greco, Alessandro Suglia, Pierpaolo Basile, Gaetano Rossiello, Giovanni Semeraro
People have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user context and preferences.