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Fenia Christopoulou

Fenia Christopoulou has 2 papers in our library, in 2019, most of them on Information extraction; often with Makoto Miwa, Sophia Ananiadou, Sunil Kumar Sahu.

2 papers in our library · 2019

OpenAlex · ORCID · h-index 8 · 29 works · 807 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 132 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3074

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 ManchesterManchester · United Kingdom2018–2022 · 14 papers🏢Huawei TechnologiesShenzhen · China2022 · 3 papers🔬Artificial Intelligence Research CenterTokyo · Japan2018–2019 · 2 papers🔬National Institute of Advanced Industrial Science and TechnologyTsukuba · Japan2018–2019 · 2 papers🎓National Technical University of AthensAthens · Greece2016–2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Concepts, shelves and topics

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

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksBiochemistry, Genetics and Molecular Biology · 1 workComputer Vision and Pattern Recognition · 1 workMolecular Biology · 1 work

🧭 Topics 1Research subjects and languages the work is about.Information extraction · 2 works

 Works by year

2010s

20192 papersFenia Christopoulou's papers of 2019

 Papers

The most cited first (OpenAlex and our library).

  1. Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network (2019 · 299 citations)
    Relations between named entities in scientific text often cross sentence boundaries. Tools built for single sentences, such as one dependency tree or a sequential RNN/CNN encoder, have trouble linking evidence that sits far apart. The authors tackle document-level relation extraction by turning an entire abstract…
  2. Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs (2019 · 296 citations)
    The paper addresses document-level relation extraction (RE), where a relation between two entities often appears only across sentences, through chains of mentions.

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. Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural NetworkAnnual Meeting of the Association for Computational Linguistics (ACL)Sunil Kumar Sahu, Fenia Christopoulou, Makoto Miwa, et al.2019 · 299 citations
  2. Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented GraphsConference on Empirical Methods in Natural Language Processing (EMNLP)Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou2019 · 296 citations
  3. PanGu-Coder: Program Synthesis with Function-Level Language ModelingarXiv:2207.11280 · original ↗ · no summary yetFenia Christopoulou et al.2022 · 49 citations

 Co-authors here

3 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 132 times (of the 141,840 papers whose bibliographies we read); 8 citing papers with a page here →.

Updated

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