no code implementations • NAACL (SMM4H) 2021 • Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-Garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre, Salvador Lima López, Ivan Flores, Karen O’Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan Banda, Martin Krallinger, Graciela Gonzalez-Hernandez
The global growth of social media usage over the past decade has opened research avenues for mining health related information that can ultimately be used to improve public health.
no code implementations • SMM4H (COLING) 2022 • Davy Weissenbacher, Juan Banda, Vera Davydova, Darryl Estrada Zavala, Luis Gasco Sánchez, Yao Ge, Yuting Guo, Ari Klein, Martin Krallinger, Mathias Leddin, Arjun Magge, Raul Rodriguez-Esteban, Abeed Sarker, Lucia Schmidt, Elena Tutubalina, Graciela Gonzalez-Hernandez
For the past seven years, the Social Media Mining for Health Applications (#SMM4H) shared tasks have promoted the community-driven development and evaluation of advanced natural language processing systems to detect, extract, and normalize health-related information in public, user-generated content.
no code implementations • ALTA 2021 • Yuting Guo, Yao Ge, Ruqi Liao, Abeed Sarker
This paper describes our approach for the automatic grading of evidence task from the Australasian Language Technology Association (ALTA) Shared Task 2021.
no code implementations • ALTA 2020 • Yuting Guo, Xiangjue Dong, Mohammed Ali Al-Garadi, Abeed Sarker, Cecile Paris, Diego Mollá Aliod
We compare three pre-trained language models, RoBERTa-base, BERTweet and ClinicalBioBERT in terms of classification accuracy.
no code implementations • NAACL (SMM4H) 2021 • Yuting Guo, Yao Ge, Mohammed Ali Al-Garadi, Abeed Sarker
This paper describes our approach for six classification tasks (Tasks 1a, 3a, 3b, 4 and 5) and one span detection task (Task 1b) from the Social Media Mining for Health (SMM4H) 2021 shared tasks.
no code implementations • SMM4H (COLING) 2020 • Ari Klein, Ilseyar Alimova, Ivan Flores, Arjun Magge, Zulfat Miftahutdinov, Anne-Lyse Minard, Karen O’Connor, Abeed Sarker, Elena Tutubalina, Davy Weissenbacher, Graciela Gonzalez-Hernandez
The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics.
no code implementations • EMNLP (WNUT) 2020 • Yuting Guo, Mohammed Ali Al-Garadi, Abeed Sarker
This paper describes the system developed by the Emory team for the WNUT-2020 Task 2: “Identifi- cation of Informative COVID-19 English Tweet”.
no code implementations • 27 Mar 2024 • Yuting Guo, Anthony Ovadje, Mohammed Ali Al-Garadi, Abeed Sarker
We developed three approaches for leveraging LLMs for text classification: employing LLMs as zero-shot classifiers, us-ing LLMs as annotators to annotate training data for supervised classifiers, and utilizing LLMs with few-shot examples for augmentation of manually annotated data.
no code implementations • 26 Feb 2024 • Seibi Kobara, Alireza Rafiei, Masoud Nateghi, Selen Bozkurt, Rishikesan Kamaleswaran, Abeed Sarker
This analysis highlighted not only the utility of NLP techniques in unstructured social media data to identify self-reported breast cancer posts, medication usage patterns, and treatment side effects but also the richness of social data on such clinical questions.
no code implementations • 2 Feb 2024 • Seyedeh Somayyeh Mousavi, Yuting Guo, Abeed Sarker, Reza Sameni
Hypertension is a global health concern with an increasing prevalence, underscoring the need for effective monitoring and analysis of blood pressure (BP) dynamics.
no code implementations • 2 Feb 2024 • Yuting Guo, Seyedeh Somayyeh Mousavi, Reza Sameni, Abeed Sarker
Based on the automatically-extracted information from these articles, we conducted an analysis of the variations of BP values across biological sex.
no code implementations • 23 Dec 2022 • Yuting Guo, Swati Rajwal, Sahithi Lakamana, Chia-Chun Chiang, Paul C. Menell, Adnan H. Shahid, Yi-Chieh Chen, Nikita Chhabra, Wan-Ju Chao, Chieh-Ju Chao, Todd J. Schwedt, Imon Banerjee, Abeed Sarker
In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem.
no code implementations • 18 Nov 2022 • Abeed Sarker
We developed a sophisticated end-to-end pipeline for mining information about nonmedical prescription medication use from social media, namely Twitter and Reddit.
no code implementations • 21 Apr 2022 • Yao Ge, Yuting Guo, Yuan-Chi Yang, Mohammed Ali Al-Garadi, Abeed Sarker
We aimed to conduct a systematic review to explore the state of FSL methods for medical NLP.
no code implementations • 29 Sep 2019 • Anahita Davoudi, Ari Z. Klein, Abeed Sarker, Graciela Gonzalez-Hernandez
Our approach obtains F_1 scores of 0. 7 for the "bot" class, representing improvements of 0. 339.
no code implementations • WS 2019 • Davy Weissenbacher, Abeed Sarker, Arjun Magge, Ashlynn Daughton, Karen O{'}Connor, Michael J. Paul, Gonzalez-Hern, Graciela ez
We present the Social Media Mining for Health Shared Tasks collocated with the ACL at Florence in 2019, which address these challenges for health monitoring and surveillance, utilizing state of the art techniques for processing noisy, real-world, and substantially creative language expressions from social media users.
no code implementations • 10 Apr 2019 • Davy Weissenbacher, Abeed Sarker, Ari Klein, Karen O'Connor, Arjun Magge Ranganatha, Graciela Gonzalez-Hernandez
A fundamental step to incorporating Twitter data in pharmacoepidemiological research is to automatically recognize medication mentions in tweets.
no code implementations • 22 Oct 2018 • Ari Z. Klein, Abeed Sarker, Davy Weissenbacher, Graciela Gonzalez-Hernandez
The primary objective of this study was to take the first step towards scaling the use of social media for observing pregnancies with birth defect outcomes, namely, developing methods for automatically detecting tweets by users reporting their birth defect outcomes.
no code implementations • WS 2018 • Davy Weissenbacher, Abeed Sarker, Michael J. Paul, Gonzalez-Hern, Graciela ez
The goals of the SMM4H shared tasks are to release annotated social media based health related datasets to the research community, and to compare the performances of natural language processing and machine learning systems on tasks involving these datasets.
1 code implementation • 4 Jun 2018 • Abeed Sarker, Graciela Gonzalez-Hernandez
Our proposed spelling variant generator has several advantages over the current state-of-the-art and other types of variant generators-(i) it is capable of filtering out lexically similar but semantically dissimilar terms, (ii) the number of variants generated is low as many low-frequency and ambiguous misspellings are filtered out, and (iii) the system is fully automatic, customizable and easily executable.
no code implementations • SEMEVAL 2017 • Abeed Sarker, Graciela Gonzalez
We present a simple supervised text classification system that combines sparse and dense vector representations of words, and generalized representations of words via clusters.
no code implementations • WS 2017 • Ari Klein, Abeed Sarker, Masoud Rouhizadeh, Karen O{'}Connor, Graciela Gonzalez
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.
no code implementations • 25 Jun 2017 • Abeed Sarker, Diego Molla, Cecile Paris
We envision that this survey will serve as a first resource for the development of future operational text summarisation techniques for EBM.
no code implementations • 8 Feb 2017 • Pramod Bharadwaj Chandrashekar, Arjun Magge, Abeed Sarker, Graciela Gonzalez
We hypothesize that we can use social media to identify cohorts of pregnant women and follow them over time to analyze crucial health-related information.
no code implementations • WS 2016 • Abeed Sarker, Graciela Gonzalez
In this paper, we discuss the preparation of these guidelines, outline the data sets prepared, and present an overview of our state-of-the-art systems for data collection, supervised classification, and information extraction.
no code implementations • 8 Oct 2016 • Abbas Chokor, Abeed Sarker, Graciela Gonzalez
Adverse reactions caused by drugs following their release into the market are among the leading causes of death in many countries.
no code implementations • 22 Jun 2016 • Abeed Sarker
We present a simple approach for automatically extracting the number of subjects involved in randomised controlled trials (RCT).