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OzU-NLP at TREC NEWS 2019: Entity ranking

Title	OzU-NLP at TREC NEWS 2019: Entity ranking
Author	Fayoumi, Kenan, Yeniterzi, R.
Publication Date:	2019
Publication Place	- National Institute of Standards and Technology (NIST)
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2-s2.0-85180124234
Record ID	4f525774-1531-4079-bfba-4420fe3dd315
Date	2019
Sample Text	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.

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OzU-NLP at TREC NEWS 2019: Entity ranking

Author Fayoumi, Kenan, Yeniterzi, R.

Publication Date 2019

Publication Place - National Institute of Standards and Technology (NIST)

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2-s2.0-85180124234

Record ID 4f525774-1531-4079-bfba-4420fe3dd315

Date 2019

Sample Text 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.