Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Participation in TREC 2020 COVID Track Using Continuous Active Learning (2011.01453v1)

Published 3 Nov 2020 in cs.IR

Abstract: We describe our participation in all five rounds of the TREC 2020 COVID Track (TREC-COVID). The goal of TREC-COVID is to contribute to the response to the COVID-19 pandemic by identifying answers to many pressing questions and building infrastructure to improve search systems [8]. All five rounds of this Track challenged participants to perform a classic ad-hoc search task on the new data collection CORD-19. Our solution addressed this challenge by applying the Continuous Active Learning model (CAL) and its variations. Our results showed us to be amongst the top scoring manual runs and we remained competitive within all categories of submissions.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (3)
  1. Xue Jun Wang (1 paper)
  2. Maura R. Grossman (6 papers)
  3. Seung Gyu Hyun (7 papers)
Citations (2)

Summary

We haven't generated a summary for this paper yet.