XCoref: Cross-document Coreference Resolution in the Wild

Cite This

Files in this item

Files Size Format View

There are no files associated with this item.

ZHUKOVA, Anastasia, Felix HAMBORG, Karsten DONNAY, Bela GIPP, 2022. XCoref: Cross-document Coreference Resolution in the Wild. iConference 2022 : Information for a Better World: shaping the global future. Virtual Event, Feb 28, 2022 - Mar 4, 2022. In: SMITS, Malte, ed.. Information for a better world: shaping the global future : 17th international conference, iConference 2022, virtual event, February 28 - March 4, 2022 : proceedings, part 1. Cham:Springer Nature, pp. 272-291. ISBN 978-3-030-96956-1. Available under: doi: 10.1007/978-3-030-96957-8_25

@inproceedings{Zhukova2022XCore-57335, title={XCoref: Cross-document Coreference Resolution in the Wild}, year={2022}, doi={10.1007/978-3-030-96957-8_25}, number={13192}, isbn={978-3-030-96956-1}, address={Cham}, publisher={Springer Nature}, series={Lecture Notes in Computer Science}, booktitle={Information for a better world: shaping the global future : 17th international conference, iConference 2022, virtual event, February 28 - March 4, 2022 : proceedings, part 1}, pages={272--291}, editor={Smits, Malte}, author={Zhukova, Anastasia and Hamborg, Felix and Donnay, Karsten and Gipp, Bela} }

<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/57335"> <dc:language>eng</dc:language> <dcterms:title>XCoref: Cross-document Coreference Resolution in the Wild</dcterms:title> <dc:contributor>Hamborg, Felix</dc:contributor> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/57335"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dcterms:issued>2022</dcterms:issued> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:contributor>Donnay, Karsten</dc:contributor> <dc:creator>Gipp, Bela</dc:creator> <dc:creator>Zhukova, Anastasia</dc:creator> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-04-22T08:49:50Z</dc:date> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-04-22T08:49:50Z</dcterms:available> <dc:contributor>Zhukova, Anastasia</dc:contributor> <dcterms:abstract xml:lang="eng">Datasets and methods for cross-document coreference resolution (CDCR) focus on events or entities with strict coreference relations. They lack, however, annotating and resolving coreference mentions with more abstract or loose relations that may occur when news articles report about controversial and polarized events. Bridging and loose coreference relations trigger associations that may expose news readers to bias by word choice and labeling. For example, coreferential mentions of “direct talks between U.S. President Donald Trump and Kim” such as “an extraordinary meeting following months of heated rhetoric” or “great chance to solve a world problem” form a more positive perception of this event. A step towards bringing awareness of bias by word choice and labeling is the reliable resolution of coreferences with high lexical diversity. We propose an unsupervised method named XCoref, which is a CDCR method that capably resolves not only previously prevalent entities, such as persons, e.g., “Donald Trump,” but also abstractly defined concepts, such as groups of persons, “caravan of immigrants,” events and actions, e.g., “marching to the U.S. border.” In an extensive evaluation, we compare the proposed XCoref to a state-of-the-art CDCR method and a previous method TCA that resolves such complex coreference relations and find that XCoref outperforms these methods. Outperforming an established CDCR model shows that the new CDCR models need to be evaluated on semantically complex mentions with more loose coreference relations to indicate their applicability of models to resolve mentions in the “wild” of political news articles.</dcterms:abstract> <dc:contributor>Gipp, Bela</dc:contributor> <dc:creator>Hamborg, Felix</dc:creator> <dc:creator>Donnay, Karsten</dc:creator> </rdf:Description> </rdf:RDF>

This item appears in the following Collection(s)

Search KOPS


My Account