@inproceedings{sneyd-stevenson-2019-modelling,
title = "Modelling Stopping Criteria for Search Results using {P}oisson Processes",
author = "Sneyd, Alison and
Stevenson, Mark",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1351",
doi = "10.18653/v1/D19-1351",
pages = "3484--3489",
abstract = "Text retrieval systems often return large sets of documents, particularly when applied to large collections. Stopping criteria can reduce the number of these documents that need to be manually evaluated for relevance by predicting when a suitable level of recall has been achieved. In this work, a novel method for determining a stopping criterion is proposed that models the rate at which relevant documents occur using a Poisson process. This method allows a user to specify both a minimum desired level of recall to achieve and a desired probability of having achieved it. We evaluate our method on a public dataset and compare it with previous techniques for determining stopping criteria.",
}
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%0 Conference Proceedings
%T Modelling Stopping Criteria for Search Results using Poisson Processes
%A Sneyd, Alison
%A Stevenson, Mark
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F sneyd-stevenson-2019-modelling
%X Text retrieval systems often return large sets of documents, particularly when applied to large collections. Stopping criteria can reduce the number of these documents that need to be manually evaluated for relevance by predicting when a suitable level of recall has been achieved. In this work, a novel method for determining a stopping criterion is proposed that models the rate at which relevant documents occur using a Poisson process. This method allows a user to specify both a minimum desired level of recall to achieve and a desired probability of having achieved it. We evaluate our method on a public dataset and compare it with previous techniques for determining stopping criteria.
%R 10.18653/v1/D19-1351
%U https://aclanthology.org/D19-1351
%U https://doi.org/10.18653/v1/D19-1351
%P 3484-3489
Markdown (Informal)
[Modelling Stopping Criteria for Search Results using Poisson Processes](https://aclanthology.org/D19-1351) (Sneyd & Stevenson, EMNLP-IJCNLP 2019)
ACL
- Alison Sneyd and Mark Stevenson. 2019. Modelling Stopping Criteria for Search Results using Poisson Processes. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3484–3489, Hong Kong, China. Association for Computational Linguistics.