Zobrazeno 1 - 10
of 75
pro vyhledávání: '"NANNI, FEDERICO"'
Autor:
Williams, Angus R., Burke-Moore, Liam, Chan, Ryan Sze-Yin, Enock, Florence E., Nanni, Federico, Sippy, Tvesha, Chung, Yi-Ling, Gabasova, Evelina, Hackenburg, Kobi, Bright, Jonathan
Advances in large language models have raised concerns about their potential use in generating compelling election disinformation at scale. This study presents a two-part investigation into the capabilities of LLMs to automate stages of an election d
Externí odkaz:
http://arxiv.org/abs/2408.06731
Autor:
Chan, Ryan Sze-Yin, Nanni, Federico, Brown, Edwin, Chapman, Ed, Williams, Angus R., Bright, Jonathan, Gabasova, Evelina
Recent surge in Large Language Model (LLM) availability has opened exciting avenues for research. However, efficiently interacting with these models presents a significant hurdle since LLMs often reside on proprietary or self-hosted API endpoints, ea
Externí odkaz:
http://arxiv.org/abs/2408.11847
There is increasing interest to work with user generated content in social media, especially textual posts over time. Currently there is no consistent way of segmenting user posts into timelines in a meaningful way that improves the quality and cost
Externí odkaz:
http://arxiv.org/abs/2303.05891
Identifying changes in individuals' behaviour and mood, as observed via content shared on online platforms, is increasingly gaining importance. Most research to-date on this topic focuses on either: (a) identifying individuals at risk or with a certa
Externí odkaz:
http://arxiv.org/abs/2205.05593
Autor:
Ardanuy, Mariona Coll, Hosseini, Kasra, McDonough, Katherine, Krause, Amrey, van Strien, Daniel, Nanni, Federico
Recognizing toponyms and resolving them to their real-world referents is required for providing advanced semantic access to textual data. This process is often hindered by the high degree of variation in toponyms. Candidate selection is the task of i
Externí odkaz:
http://arxiv.org/abs/2009.08114
Autor:
Ardanuy, Mariona Coll, Nanni, Federico, Beelen, Kaspar, Hosseini, Kasra, Ahnert, Ruth, Lawrence, Jon, McDonough, Katherine, Tolfo, Giorgia, Wilson, Daniel CS, McGillivray, Barbara
This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. In particular, this work is focused on atypical animacy and examines the scenario in which typically inanimate obj
Externí odkaz:
http://arxiv.org/abs/2005.11140
Autor:
Nanni, Federico, Glavas, Goran, Rehbein, Ines, Ponzetto, Simone Paolo, Stuckenschmidt, Heiner
During the last fifteen years, automatic text scaling has become one of the key tools of the Text as Data community in political science. Prominent text scaling algorithms, however, rely on the assumption that latent positions can be captured just by
Externí odkaz:
http://arxiv.org/abs/1904.06217
Publikováno v:
J. Comput. Cult. Herit. 14, 1, Article 2 (February 2021)
The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source. In this paper,
Externí odkaz:
http://arxiv.org/abs/1904.05439
Retrieving paragraphs to populate a Wikipedia article is a challenging task. The new TREC Complex Answer Retrieval (TREC CAR) track introduces a comprehensive dataset that targets this retrieval scenario. We present early results from a variety of ap
Externí odkaz:
http://arxiv.org/abs/1705.04803
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.