Search Results for author: James Ravenscroft

Found 7 papers, 2 papers with code

A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering

1 code implementation30 Nov 2022 Matthew Maufe, James Ravenscroft, Rob Procter, Maria Liakata

Question Answering (QA) is a growing area of research, often used to facilitate the extraction of information from within documents.

Question Answering

CD\^2CR: Co-reference resolution across documents and domains

no code implementations EACL 2021 James Ravenscroft, Amanda Clare, Arie Cattan, Ido Dagan, Maria Liakata

Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents.

CD2CR: Co-reference Resolution Across Documents and Domains

1 code implementation29 Jan 2021 James Ravenscroft, Arie Cattan, Amanda Clare, Ido Dagan, Maria Liakata

Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents.

Measuring prominence of scientific work in online news as a proxy for impact

no code implementations28 Jul 2020 James Ravenscroft, Amanda Clare, Maria Liakata

We find that Impact Case studies submitted to the UK Research Excellence Framework (REF) 2014 that refer to scientific papers mentioned in newspaper articles were awarded a higher score in the REF assessment.

Semantic Similarity Semantic Textual Similarity

HarriGT: A Tool for Linking News to Science

no code implementations ACL 2018 James Ravenscroft, Am Clare, a, Maria Liakata

Being able to reliably link scientific works to the newspaper articles that discuss them could provide a breakthrough in the way we rationalise and measure the impact of science on our society.

Document Classification Entity Extraction using GAN +2

Multi-label Annotation in Scientific Articles - The Multi-label Cancer Risk Assessment Corpus

no code implementations LREC 2016 James Ravenscroft, Anika Oellrich, Shyamasree Saha, Maria Liakata

Here, we present the Multi-CoreSC CRA corpus, a text corpus specific to the domain of cancer risk assessment (CRA), consisting of 50 full text papers, each of which contains sentences annotated with one or more CoreSCs.

Sentence

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