@inproceedings{metzger-etal-2020-smm4h,
title = "{SMM}4{H} Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from {T}witter: Machine Learning, Deep Learning, and Post-Processing",
author = "Metzger, Isabel and
Haskovic, Emir Y. and
Black, Allison and
Yi, Whitley M. and
Chandra, Rajat S. and
Rutledge, Mark T. and
McMahon, William and
Aphinyanaphongs, Yindalon",
editor = "Gonzalez-Hernandez, Graciela and
Klein, Ari Z. and
Flores, Ivan and
Weissenbacher, Davy and
Magge, Arjun and
O'Connor, Karen and
Sarker, Abeed and
Minard, Anne-Lyse and
Tutubalina, Elena and
Miftahutdinov, Zulfat and
Alimova, Ilseyar",
booktitle = "Proceedings of the Fifth Social Media Mining for Health Applications Workshop {\&} Shared Task",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.smm4h-1.9",
pages = "57--62",
abstract = "This paper presents our approach to multi-class text categorization of tweets mentioning prescription medications as being indicative of potential abuse/misuse (A), consumption/non-abuse (C), mention-only (M), or an unrelated reference (U) using natural language processing techniques. Data augmentation increased our training and validation corpora from 13,172 tweets to 28,094 tweets. We also created word-embeddings on domain-specific social media and medical corpora. Our hybrid pipeline of an attention-based CNN with post-processing was the best performing system in task 4 of SMM4H 2020, with an F1 score of 0.51 for class A.",
}
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<abstract>This paper presents our approach to multi-class text categorization of tweets mentioning prescription medications as being indicative of potential abuse/misuse (A), consumption/non-abuse (C), mention-only (M), or an unrelated reference (U) using natural language processing techniques. Data augmentation increased our training and validation corpora from 13,172 tweets to 28,094 tweets. We also created word-embeddings on domain-specific social media and medical corpora. Our hybrid pipeline of an attention-based CNN with post-processing was the best performing system in task 4 of SMM4H 2020, with an F1 score of 0.51 for class A.</abstract>
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%0 Conference Proceedings
%T SMM4H Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from Twitter: Machine Learning, Deep Learning, and Post-Processing
%A Metzger, Isabel
%A Haskovic, Emir Y.
%A Black, Allison
%A Yi, Whitley M.
%A Chandra, Rajat S.
%A Rutledge, Mark T.
%A McMahon, William
%A Aphinyanaphongs, Yindalon
%Y Gonzalez-Hernandez, Graciela
%Y Klein, Ari Z.
%Y Flores, Ivan
%Y Weissenbacher, Davy
%Y Magge, Arjun
%Y O’Connor, Karen
%Y Sarker, Abeed
%Y Minard, Anne-Lyse
%Y Tutubalina, Elena
%Y Miftahutdinov, Zulfat
%Y Alimova, Ilseyar
%S Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F metzger-etal-2020-smm4h
%X This paper presents our approach to multi-class text categorization of tweets mentioning prescription medications as being indicative of potential abuse/misuse (A), consumption/non-abuse (C), mention-only (M), or an unrelated reference (U) using natural language processing techniques. Data augmentation increased our training and validation corpora from 13,172 tweets to 28,094 tweets. We also created word-embeddings on domain-specific social media and medical corpora. Our hybrid pipeline of an attention-based CNN with post-processing was the best performing system in task 4 of SMM4H 2020, with an F1 score of 0.51 for class A.
%U https://aclanthology.org/2020.smm4h-1.9
%P 57-62
Markdown (Informal)
[SMM4H Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from Twitter: Machine Learning, Deep Learning, and Post-Processing](https://aclanthology.org/2020.smm4h-1.9) (Metzger et al., SMM4H 2020)
ACL
- Isabel Metzger, Emir Y. Haskovic, Allison Black, Whitley M. Yi, Rajat S. Chandra, Mark T. Rutledge, William McMahon, and Yindalon Aphinyanaphongs. 2020. SMM4H Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from Twitter: Machine Learning, Deep Learning, and Post-Processing. In Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, pages 57–62, Barcelona, Spain (Online). Association for Computational Linguistics.