OzU-NLP at TREC NEWS 2019: Entity ranking

Fayoumi, Kenan; Yeniterzi, R.

dc.contributor.author	Fayoumi, Kenan
dc.contributor.author	Yeniterzi, R.
dc.date.accessioned	2024-03-08T13:18:49Z
dc.date.available	2024-03-08T13:18:49Z
dc.date.issued	2019
dc.identifier.uri	http://hdl.handle.net/10679/9285
dc.identifier.uri	https://trec.nist.gov/pubs/trec28/papers/OzUNLP.News.pdf
dc.description.abstract	This 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.	en_US
dc.language.iso	eng	en_US
dc.publisher	National Institute of Standards and Technology (NIST)	en_US
dc.relation.ispartof	28th Text REtrieval Conference, TREC 2019 - Proceedings
dc.rights	restrictedAccess
dc.title	OzU-NLP at TREC NEWS 2019: Entity ranking	en_US
dc.type	Conference paper	en_US
dc.publicationstatus	Published	en_US
dc.contributor.department	Özyeğin University
dc.identifier.scopus	SCOPUS:2-s2.0-85180124234
dc.contributor.ozugradstudent	Fayoumi, Kenan
dc.relation.publicationcategory	Conference Paper - International - Institutional Graduate Student