Football Coreference Corpus
Count of file(s): 1
Date
2020
Description
This script generates:
1. the original sentence-level Football Coreference Corpus (*FCC*),
2. a version of the sentence-level FCC which was cleaned and updated after manual review,
3. *FCC-T*, the extended version of the Football Coreference Corpus with reannotated token-level spans,
4. and publication date annotations for the *ECB+* corpus.
The script downloads the original documents from archive.org's WaybackMachine, cleans and processes them locally on your
machine and combines the result with our annotations. See README.md for instructions.
Details on the annotations and corpora:
* For the original FCC, see Bugert et al. 2020 "Breaking the Subtopic Barrier in Cross-Document Event Coreference Resolution", http://ceur-ws.org/Vol-2593/paper3.pdf
* For the token-level reannotation FCC-T, see Bugert et al. 2020 "Cross-Document Event Coreference Resolution Beyond Corpus-Tailored Systems", https://arxiv.org/abs/2011.12249
In case of trouble with this downloader, please get in touch on Github: https://github.com/UKPLab/cdcr-beyond-corpus-tailored/issues
Cross-document event coreference resolution (CDCR) is the task of detecting and clustering mentions of events across a set of documents. A major bottleneck in CDCR is a lack of appropriate datasets, which stems from the difficulty of annotating data for this task. We present the first scalable approach for annotating cross-subtopic event coreference links, a highly valuable but rarely occurring type of cross-document link. The annotation of these links requires combing through hundreds of documents - an endeavor for which conventional token-level annotation schemes with trained expert annotators are too expensive. We instead propose crowdsourcing annotation on sentence level to achieve scalability. We apply our approach to create the Football Coreference Corpus (FCC), a corpus of 451 sports news reports, while reaching high agreement between NLP experts and crowd annotators in the process.
DFG subject classification
4.43-04 Künstliche Intelligenz und Maschinelle Lernverfahren4.43-05 Bild- und Sprachverarbeitung, Computergraphik und Visualisierung, Human Computer Interaction, Ubiquitous und Wearable Computing
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