@inproceedings{klein-etal-2017-detecting,
title = "Detecting Personal Medication Intake in {T}witter: An Annotated Corpus and Baseline Classification System",
author = "Klein, Ari and
Sarker, Abeed and
Rouhizadeh, Masoud and
O{'}Connor, Karen and
Gonzalez, Graciela",
editor = "Cohen, Kevin Bretonnel and
Demner-Fushman, Dina and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "{B}io{NLP} 2017",
month = aug,
year = "2017",
address = "Vancouver, Canada,",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2316",
doi = "10.18653/v1/W17-2316",
pages = "136--142",
abstract = "Social media sites (e.g., Twitter) have been used for surveillance of drug safety at the population level, but studies that focus on the effects of medications on specific sets of individuals have had to rely on other sources of data. Mining social media data for this in-formation would require the ability to distinguish indications of personal medication in-take in this media. Towards that end, this paper presents an annotated corpus that can be used to train machine learning systems to determine whether a tweet that mentions a medication indicates that the individual posting has taken that medication at a specific time. To demonstrate the utility of the corpus as a training set, we present baseline results of supervised classification.",
}
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<abstract>Social media sites (e.g., Twitter) have been used for surveillance of drug safety at the population level, but studies that focus on the effects of medications on specific sets of individuals have had to rely on other sources of data. Mining social media data for this in-formation would require the ability to distinguish indications of personal medication in-take in this media. Towards that end, this paper presents an annotated corpus that can be used to train machine learning systems to determine whether a tweet that mentions a medication indicates that the individual posting has taken that medication at a specific time. To demonstrate the utility of the corpus as a training set, we present baseline results of supervised classification.</abstract>
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%0 Conference Proceedings
%T Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System
%A Klein, Ari
%A Sarker, Abeed
%A Rouhizadeh, Masoud
%A O’Connor, Karen
%A Gonzalez, Graciela
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F klein-etal-2017-detecting
%X Social media sites (e.g., Twitter) have been used for surveillance of drug safety at the population level, but studies that focus on the effects of medications on specific sets of individuals have had to rely on other sources of data. Mining social media data for this in-formation would require the ability to distinguish indications of personal medication in-take in this media. Towards that end, this paper presents an annotated corpus that can be used to train machine learning systems to determine whether a tweet that mentions a medication indicates that the individual posting has taken that medication at a specific time. To demonstrate the utility of the corpus as a training set, we present baseline results of supervised classification.
%R 10.18653/v1/W17-2316
%U https://aclanthology.org/W17-2316
%U https://doi.org/10.18653/v1/W17-2316
%P 136-142
Markdown (Informal)
[Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System](https://aclanthology.org/W17-2316) (Klein et al., BioNLP 2017)
ACL