# Unsupervised Context Retrieval for Long-tail Entities

## TL;DR

This paper introduces an unsupervised method for retrieving textual contexts of long-tail entities in media streams, addressing the challenge of limited knowledge base information and demonstrating robustness for out-of-KB entities.

## Contribution

It proposes a novel unsupervised approach that leverages established entities and their contexts to improve long-tail entity monitoring.

## Key findings

- Method is effective on a custom test collection.
- Approach is robust for out-of-KB entities.
- Outperforms baseline retrieval methods.

## Abstract

Monitoring entities in media streams often relies on rich entity representations, like structured information available in a knowledge base (KB). For long-tail entities, such monitoring is highly challenging, due to their limited, if not entirely missing, representation in the reference KB. In this paper, we address the problem of retrieving textual contexts for monitoring long-tail entities. We propose an unsupervised method to overcome the limited representation of long-tail entities by leveraging established entities and their contexts as support information. Evaluation on a purpose-built test collection shows the suitability of our approach and its robustness for out-of-KB entities.