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Nicola De Cao

Nicola De Cao has 2 papers in our library, in 2019–2020, most of them on Named entity recognition, Question answering, Evaluation & benchmarks; often with Aleksandra Piktus, Angela Fan, Fabio Petroni.

2 papers in our library · 2019–2020

OpenAlex · ORCID · h-index 18 · 43 works · 2,927 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 3,826 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #2549

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of AmsterdamAmsterdam · Netherlands2018–2022 · 16 papers🎓University of EdinburghEdinburgh · United Kingdom2019–2022 · 7 papers🏢Google DeepMindLondon · United Kingdom2024 · 2 papers🎓University of CopenhagenCopenhagen · Denmark2024 · 2 papers🎓University of PisaPisa · Italy2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 4Research subjects and languages the work is about.Evaluation & benchmarks · 1 workNamed entity recognition · 1 workQuestion answering · 1 workReasoning · 1 work

 Works by year

2020s

20201 paperKILT: a Benchmark for Knowledge Intensive Language Tasks

2010s

20191 paperQuestion Answering by Reasoning Across Documents with Graph Convolutional Networks

 Papers

The most cited first (OpenAlex and our library).

  1. KILT: a Benchmark for Knowledge Intensive Language Tasks (2020 · 665 citations)
    Benchmarks for knowledge-heavy NLP tasks, such as open-domain QA, fact verification and entity linking, have each used their own Wikipedia version or corpus, file format and evaluation scripts.
  2. Question Answering by Reasoning Across Documents with Graph Convolutional Networks (2019 · 136 citations)
    Most reading comprehension systems answer questions from a single document or paragraph. This paper targets multi-hop question answering, where the answer can only be reached by chaining evidence spread over several documents. The authors propose Entity-GCN and evaluate it on the WikiHop dataset (built from WIKIPEDIA…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. KILT: a Benchmark for Knowledge Intensive Language TasksFabio Petroni, Aleksandra Piktus, Angela Fan, et al.Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) · 665 citations
  2. Multilingual Autoregressive Entity Linking · original ↗ · no summary yet · Transactions of the Association for Computational LinguisticsNicola De Cao et al.2022 · 153 citations
  3. Question Answering by Reasoning Across Documents with Graph Convolutional NetworksNicola De Cao, Wilker Aziz, Ivan TitovConference of the North American Chapter of the Association for Computational Linguistics (NAACL) · 136 citations

 Co-authors here

11 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 3,826 times (of the 141,840 papers whose bibliographies we read); 277 citing papers with a page here →.

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