@inproceedings{8af01794cba849f69a10bed9004f7448,
title = "Classifying Negative Findings in Biomedical Publications",
abstract = "Publication bias refers to the phenomenon that statistically significant, “positive” results are more likely to be published than non-significant, “negative” results. Currently, researchers have to manually identify negative results in a large number of publications in order to examine publication biases. This paper proposes an NLP approach for automatically classifying negated sentences in biomedical abstracts as either reporting negative findings or not. Using multinomial na{\"i}ve Bayes algorithm and bag-of-words features enriched by parts-of-speeches and constituents, we built a classifier that reached 84% accuracy based on 5-fold cross validation on a balanced data set.",
author = "Bei Yu and Daniele Fanelli",
note = "Publisher Copyright: {\textcopyright}2014 Association for Computational Linguistics; ACL 2014 Workshop on Biomedical Natural Language Processing, BioNLP 2014 ; Conference date: 27-06-2014 Through 28-06-2014",
year = "2014",
doi = "10.3115/v1/w14-3403",
language = "English (US)",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "19--23",
booktitle = "ACL 2014 - BioNLP 2014, Workshop on Biomedical Natural Language Processing, Proceedings of the Workshop",
address = "United States",
}