@inproceedings{bordea-etal-2019-query,
title = "Query selection methods for automated corpora construction with a use case in food-drug interactions",
author = "Bordea, Georgeta and
Randriatsitohaina, Tsanta and
Mougin, Fleur and
Grabar, Natalia and
Hamon, Thierry",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5013",
doi = "10.18653/v1/W19-5013",
pages = "115--124",
abstract = "In this paper, we address the problem of automatically constructing a relevant corpus of scientific articles about food-drug interactions. There is a growing number of scientific publications that describe food-drug interactions but currently building a high-coverage corpus that can be used for information extraction purposes is not trivial. We investigate several methods for automating the query selection process using an expert-curated corpus of food-drug interactions. Our experiments show that index term features along with a decision tree classifier are the best approach for this task and that feature selection approaches and in particular gain ratio outperform frequency-based methods for query selection.",
}
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<abstract>In this paper, we address the problem of automatically constructing a relevant corpus of scientific articles about food-drug interactions. There is a growing number of scientific publications that describe food-drug interactions but currently building a high-coverage corpus that can be used for information extraction purposes is not trivial. We investigate several methods for automating the query selection process using an expert-curated corpus of food-drug interactions. Our experiments show that index term features along with a decision tree classifier are the best approach for this task and that feature selection approaches and in particular gain ratio outperform frequency-based methods for query selection.</abstract>
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%0 Conference Proceedings
%T Query selection methods for automated corpora construction with a use case in food-drug interactions
%A Bordea, Georgeta
%A Randriatsitohaina, Tsanta
%A Mougin, Fleur
%A Grabar, Natalia
%A Hamon, Thierry
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F bordea-etal-2019-query
%X In this paper, we address the problem of automatically constructing a relevant corpus of scientific articles about food-drug interactions. There is a growing number of scientific publications that describe food-drug interactions but currently building a high-coverage corpus that can be used for information extraction purposes is not trivial. We investigate several methods for automating the query selection process using an expert-curated corpus of food-drug interactions. Our experiments show that index term features along with a decision tree classifier are the best approach for this task and that feature selection approaches and in particular gain ratio outperform frequency-based methods for query selection.
%R 10.18653/v1/W19-5013
%U https://aclanthology.org/W19-5013
%U https://doi.org/10.18653/v1/W19-5013
%P 115-124
Markdown (Informal)
[Query selection methods for automated corpora construction with a use case in food-drug interactions](https://aclanthology.org/W19-5013) (Bordea et al., BioNLP 2019)
ACL