Publication:
OzU-NLP at TREC NEWS 2019: Entity ranking

dc.contributor.authorFayoumi, Kenan
dc.contributor.authorYeniterzi, R.
dc.contributor.ozugradstudentFayoumi, Kenan
dc.date.accessioned2024-03-08T13:18:49Z
dc.date.available2024-03-08T13:18:49Z
dc.date.issued2019
dc.description.abstractThis paper presents our work and submission for TREC 2019 News Track: Entity Ranking Task. Our approach utilizes Doc2Vec's ability to represent documents as fixed sized numerical vectors. Applied on news articles and wiki-pages of the entities, Doc2Vec provides us with vector representations for these two that we can utilize to perform ranking on entities. We also investigate whether background linked articles can be useful for entity ranking task.
dc.identifier.scopus2-s2.0-85180124234
dc.identifier.urihttp://hdl.handle.net/10679/9285
dc.language.isoeng
dc.publicationstatusPublished
dc.publisherNational Institute of Standards and Technology (NIST)
dc.relation.ispartof28th Text REtrieval Conference, TREC 2019 - Proceedings
dc.relation.publicationcategoryInternational
dc.rightsrestrictedAccess
dc.titleOzU-NLP at TREC NEWS 2019: Entity ranking
dc.typeconferenceObject
dc.type.subtypeConference paper
dspace.entity.typePublication

Files

License bundle

Now showing 1 - 1 of 1
Placeholder
Name:
license.txt
Size:
1.45 KB
Format:
Item-specific license agreed upon to submission
Description:

Collections