@inproceedings{xherija-2018-classification,
title = "Classification of Medication-Related Tweets Using Stacked Bidirectional {LSTM}s with Context-Aware Attention",
author = "Xherija, Orest",
editor = "Gonzalez-Hernandez, Graciela and
Weissenbacher, Davy and
Sarker, Abeed and
Paul, Michael",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5910",
doi = "10.18653/v1/W18-5910",
pages = "38--42",
abstract = "This paper describes the system that team UChicagoCompLx developed for the 2018 Social Media Mining for Health Applications (SMM4H) Shared Task. We use a variant of the Message-level Sentiment Analysis (MSA) model of (Baziotis et al., 2017), a word-level stacked bidirectional Long Short-Term Memory (LSTM) network equipped with attention, to classify medication-related tweets in the four subtasks of the SMM4H Shared Task. Without any subtask-specific tuning, the model is able to achieve competitive results across all subtasks. We make the datasets, model weights, and code publicly available.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xherija-2018-classification">
<titleInfo>
<title>Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention</title>
</titleInfo>
<name type="personal">
<namePart type="given">Orest</namePart>
<namePart type="family">Xherija</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2018-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task</title>
</titleInfo>
<name type="personal">
<namePart type="given">Graciela</namePart>
<namePart type="family">Gonzalez-Hernandez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Davy</namePart>
<namePart type="family">Weissenbacher</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abeed</namePart>
<namePart type="family">Sarker</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Michael</namePart>
<namePart type="family">Paul</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Brussels, Belgium</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes the system that team UChicagoCompLx developed for the 2018 Social Media Mining for Health Applications (SMM4H) Shared Task. We use a variant of the Message-level Sentiment Analysis (MSA) model of (Baziotis et al., 2017), a word-level stacked bidirectional Long Short-Term Memory (LSTM) network equipped with attention, to classify medication-related tweets in the four subtasks of the SMM4H Shared Task. Without any subtask-specific tuning, the model is able to achieve competitive results across all subtasks. We make the datasets, model weights, and code publicly available.</abstract>
<identifier type="citekey">xherija-2018-classification</identifier>
<identifier type="doi">10.18653/v1/W18-5910</identifier>
<location>
<url>https://aclanthology.org/W18-5910</url>
</location>
<part>
<date>2018-10</date>
<extent unit="page">
<start>38</start>
<end>42</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention
%A Xherija, Orest
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%Y Sarker, Abeed
%Y Paul, Michael
%S Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F xherija-2018-classification
%X This paper describes the system that team UChicagoCompLx developed for the 2018 Social Media Mining for Health Applications (SMM4H) Shared Task. We use a variant of the Message-level Sentiment Analysis (MSA) model of (Baziotis et al., 2017), a word-level stacked bidirectional Long Short-Term Memory (LSTM) network equipped with attention, to classify medication-related tweets in the four subtasks of the SMM4H Shared Task. Without any subtask-specific tuning, the model is able to achieve competitive results across all subtasks. We make the datasets, model weights, and code publicly available.
%R 10.18653/v1/W18-5910
%U https://aclanthology.org/W18-5910
%U https://doi.org/10.18653/v1/W18-5910
%P 38-42
Markdown (Informal)
[Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention](https://aclanthology.org/W18-5910) (Xherija, EMNLP 2018)
ACL