An Obligations Extraction System for Heterogeneous Legal Documents: Building and Evaluating Data and Model

Autor: Maria Iacono, Laura Rossi, Paolo Dangelo, Andrea Tesei, Lorenzo De Mattei

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::aa281a70ba3a0f8541c7ce55e7481cda
https://doi.org/10.4000/books.aaccademia.10932

On the interaction of automatic evaluation and task framing in headline style transfer

Autor: Lorenzo De Mattei, Michele Cafagna, Huiyuan Lai, Felice Dell'Orletta, Malvina Nissim, Albert Gatt

Publikováno v: University of Groningen
Proceedings of the 1st Workshop on Evaluating NLG Evaluation (EvalNLGEval'20)

An ongoing debate in the NLG community concerns the best way to evaluate systems, with human evaluation often being considered the most reliable method, compared to corpus-based metrics. However, tasks involving subtle textual differences, such as st

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3e65bc0fb66780ce09da3f84f9aea01
http://arxiv.org/abs/2101.01634

ATE ABSITA @ EVALITA2020: Overview of the aspect term extraction and aspect-based sentiment analysis task

Autor: Alessio Miaschi, Marco Polignano, Graziella De Martino, Andrea Iovine, Lorenzo De Mattei, Giulia Rambelli

Publikováno v: EVALITA

Over the last years, the rise of novel sentiment analysis techniques to assess aspect-based opinions on product reviews has become a key component for providing valuable insights to both consumers and businesses. To this extent, we propose ATE_ABSITA

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba86d413851c255659d677ad6ef24b4f
http://hdl.handle.net/11568/1112316

Is this Sentence Difficult? Do you Agree?

Autor: Felice Dell'Orletta, Lorenzo De Mattei, Benedetta Iavarone, Giulia Venturi, Dominique Brunato

Publikováno v: EMNLP
Scopus-Elsevier
Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1–10, Brussels, 31/10/2018-04/11/2018
info:cnr-pdr/source/autori:Brunato D., De Mattei L., Dell'Orletta F., Iavarone B., Venturi G./congresso_nome:Conference on Empirical Methods in Natural Language Processing (EMNLP)/congresso_luogo:Brussels/congresso_data:31%2F10%2F2018-04%2F11%2F2018/anno:2018/pagina_da:1/pagina_a:10/intervallo_pagine:1–10

In this paper, we present a crowdsourcing-based approach to model the human perception of sentence complexity. We collect a large corpus of sentences rated with judgments of complexity for two typologically-different languages, Italian and English. W

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b20afe55a6629fdb761a47415ea243ae
https://doi.org/10.18653/v1/d18-1289