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Elastic Embedded Background Linking for News Articles with Keywords, Entities and Events

Authors: Luis Adrián Cabrera-Diego; Emanuela Boros; Antoine Doucet;

Elastic Embedded Background Linking for News Articles with Keywords, Entities and Events

Abstract

In this paper, we present a collection of five flexible background linking models created for the News Track in TREC 2021 that generate ranked lists of articles to provide contextual information. The collection is based on the use of sentence embeddings indexes, created with Sentence BERT and Open Distro for ElasticSearch. For each model, we explore additional tools, from keywords extraction using YAKE, to entity and event detection, while passing through a linear combination. The associated code is available online as open-source software.

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Keywords

Information system, Language models, Rank aggregation

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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