Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Koleva, Aneta"'
Web tables contain a large amount of valuable knowledge and have inspired tabular language models aimed at tackling table interpretation (TI) tasks. In this paper, we analyse a widely used benchmark dataset for evaluation of TI tasks, particularly fo
Externí odkaz:
http://arxiv.org/abs/2403.04577
The capabilities of large language models (LLMs) have been successfully applied in the context of table representation learning. The recently proposed tabular language models have reported state-of-the-art results across various tasks for table inter
Externí odkaz:
http://arxiv.org/abs/2309.08650
Autor:
Ringsquandl, Martin, Koleva, Aneta
Despite recent advancements in tabular language model research, real-world applications are still challenging. In industry, there is an abundance of tables found in spreadsheets, but acquisition of substantial amounts of labels is expensive, since on
Externí odkaz:
http://arxiv.org/abs/2211.04128
Specialized transformer-based models for encoding tabular data have gained interest in academia. Although tabular data is omnipresent in industry, applications of table transformers are still missing. In this paper, we study how these models can be a
Externí odkaz:
http://arxiv.org/abs/2209.14812
High-quality Web tables are rich sources of information that can be used to populate Knowledge Graphs (KG). The focus of this paper is an evaluation of methods for table-to-class annotation, which is a sub-task of Table Interpretation (TI). We provid
Externí odkaz:
http://arxiv.org/abs/2110.15132
Autor:
Abbas, Nacira, Alghamdi, Kholoud, Alinam, Mortaza, Alloatti, Francesca, Amaral, Glenda, d'Amato, Claudia, Asprino, Luigi, Beno, Martin, Bensmann, Felix, Biswas, Russa, Cai, Ling, Capshaw, Riley, Carriero, Valentina Anita, Celino, Irene, Dadoun, Amine, De Giorgis, Stefano, Delva, Harm, Domingue, John, Dumontier, Michel, Emonet, Vincent, van Erp, Marieke, Arias, Paola Espinoza, Fallatah, Omaima, Ferrada, Sebastián, Ocaña, Marc Gallofré, Georgiou, Michalis, Gesese, Genet Asefa, Gillis-Webber, Frances, Giovannetti, Francesca, Buey, Marìa Granados, Harrando, Ismail, Heibi, Ivan, Horta, Vitor, Huber, Laurine, Igne, Federico, Jaradeh, Mohamad Yaser, Keshan, Neha, Koleva, Aneta, Koteich, Bilal, Kurniawan, Kabul, Liu, Mengya, Ma, Chuangtao, Maas, Lientje, Mansfield, Martin, Mariani, Fabio, Marzi, Eleonora, Mesbah, Sepideh, Mistry, Maheshkumar, Tirado, Alba Catalina Morales, Nguyen, Anna, Nguyen, Viet Bach, Oelen, Allard, Pasqual, Valentina, Paulheim, Heiko, Polleres, Axel, Porena, Margherita, Portisch, Jan, Presutti, Valentina, Pustu-Iren, Kader, Mendez, Ariam Rivas, Roshankish, Soheil, Rudolph, Sebastian, Sack, Harald, Sakor, Ahmad, Salas, Jaime, Schleider, Thomas, Shi, Meilin, Spinaci, Gianmarco, Sun, Chang, Tietz, Tabea, Dhouib, Molka Tounsi, Umbrico, Alessandro, Berg, Wouter van den, Xu, Weiqin
One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is that of a: "Public FAIR Knowledge Graph of Everything: We increasingl
Externí odkaz:
http://arxiv.org/abs/2012.11936