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

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.