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6th SMM4H@NAACL-HLT 2021: Mexico City, Mexico
- Arjun Magge, Ari Z. Klein, Antonio Miranda-Escalada, Mohammed Ali Al-garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulàlia Farré-Maduell, Salvador Lima-López, Ivan Flores, Karen O'Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan M. Banda, Martin Krallinger, Graciela Gonzalez-Hernandez:
Proceedings of the Sixth Social Media Mining for Health Workshop and Shared Task, SMM4H@NAACL-HLT 2021, Mexico City, Mexico, June 10, 2021. Association for Computational Linguistics 2021, ISBN 978-1-954085-31-2 - Jingcheng Niu, Erin E. Rees, Victoria Ng, Gerald Penn:
Statistically Evaluating Social Media Sentiment Trends towards COVID-19 Non-Pharmaceutical Interventions with Event Studies. 1-6 - Payam Karisani, Jinho D. Choi, Li Xiong:
View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data. 7-12 - Antonio Miranda-Escalada, Eulàlia Farré-Maduell
, Salvador Lima-López, Luis Gascó, Vicent Brivá-Iglesias, Marvin M. Agüero-Torales
The ProfNER shared task on automatic recognition of occupation mentions in social media: systems, evaluation, guidelines, embeddings and corpora. 13-20 - Arjun Magge, Ari Z. Klein, Antonio Miranda-Escalada, Mohammed Ali Al-Garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulàlia Farré, Salvador Lima-López, Ivan Flores, Karen O'Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker
Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021. 21-32 - Sidharth R
BERT based Transformers lead the way in Extraction of Health Information from Social Media. 33-38 - Andrey Sakhovskiy, Zulfat Miftahutdinov, Elena Tutubalina:
KFU NLP Team at SMM4H 2021 Tasks: Cross-lingual and Cross-modal BERT-based Models for Adverse Drug Effects. 39-43 - George-Andrei Dima, Dumitru-Clementin Cercel, Mihai Dascalu:
Transformer-based Multi-Task Learning for Adverse Effect Mention Analysis in Tweets. 44-51 - Yuting Guo, Yao Ge, Mohammed Ali Al-Garadi, Abeed Sarker
Pre-trained Transformer-based Classification and Span Detection Models for Social Media Health Applications. 52-57 - Alham Fikri Aji, Made Nindyatama Nityasya, Haryo Akbarianto Wibowo, Radityo Eko Prasojo, Tirana Fatyanosa:
BERT Goes Brrr: A Venture Towards the Lesser Error in Classifying Medical Self-Reporters on Twitter. 58-64 - Alberto Valdes, Jesus Lopez, Manuel Montes:
UACH-INAOE at SMM4H: a BERT based approach for classification of COVID-19 Twitter posts. 65-68 - David Carreto Fidalgo, Daniel Vila-Suero, Francisco Aranda Montes, Ignacio Talavera:
System description for ProfNER - SMMH: Optimized finetuning of a pretrained transformer and word vectors. 69-73 - Sergio Santamaria Carrasco, Roberto Cuervo Rosillo:
Word Embeddings, Cosine Similarity and Deep Learning for Identification of Professions & Occupations in Health-related Social Media. 74-76 - Tong Zhou, Zhucong Li, Zhen Gan, Baoli Zhang, Yubo Chen
Classification, Extraction, and Normalization : CASIA_Unisound Team at the Social Media Mining for Health 2021 Shared Tasks. 77-82 - Usama Yaseen, Stefan Langer:
Neural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021. 83-87 - Tanay Kayastha, Pranjal Gupta
BERT based Adverse Drug Effect Tweet Classification. 88-90 - Mohab Elkaref, Lamiece Hassan
A Joint Training Approach to Tweet Classification and Adverse Effect Extraction and Normalization for SMM4H 2021. 91-94 - Pavel Blinov:
Text Augmentation Techniques in Drug Adverse Effect Detection Task. 95-97 - Lung-Hao Lee, Man-Chen Hung, Chien-Huan Lu, Chang-Hao Chen, Po-Lei Lee, Kuo-Kai Shyu:
Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers. 98-101 - Deepak Kumar
NLP@NISER: Classification of COVID19 tweets containing symptoms. 102-104 - Victoria Pachón, Jacinto Mata Vázquez, Juan Luis Domínguez-Olmedo:
Identification of profession & occupation in Health-related Social Media using tweets in Spanish. 105-107 - Pedro Ruas
Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification. 108-111 - Adarsh Kumar, Ojasv Kamal, Susmita Mazumdar:
Adversities are all you need: Classification of self-reported breast cancer posts on Twitter using Adversarial Fine-tuning. 112-114 - Frances Adriana Laureano De Leon, Harish Tayyar Madabushi
UoB at ProfNER 2021: Data Augmentation for Classification Using Machine Translation. 115-117 - Varad Pimpalkhute
IIITN NLP at SMM4H 2021 Tasks: Transformer Models for Classification on Health-Related Imbalanced Twitter Datasets. 118-122 - Ying Luo, Lis Pereira, Ichiro Kobayashi:
OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language models. 123-125 - Zongcheng Ji, Tian Xia, Mei Han:
PAII-NLP at SMM4H 2021: Joint Extraction and Normalization of Adverse Drug Effect Mentions in Tweets. 126-127 - Vasile Pais, Maria Mitrofan:
Assessing multiple word embeddings for named entity recognition of professions and occupations in health-related social media. 128-130 - Max Fleming, Priyanka Dondeti, Caitlin N. Dreisbach, Adam Poliak:
Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets. 131-134 - Anupam Mondal, Sainik Kumar Mahata, Monalisa Dey, Dipankar Das:
Classification of COVID19 tweets using Machine Learning Approaches. 135-137 - Rajarshi Roychoudhury, Sudip Kumar Naskar:
Fine-tuning BERT to classify COVID19 tweets containing symptoms. 138-140 - José-Alberto Mesa-Murgado, Ana Belén Parras Portillo, Pilar López-Úbeda, María Teresa Martín-Valdivia, Luis Alfonso Ureña López:
Identifying professions & occupations in Health-related Social Media using Natural Language Processing. 141-145 - Joseph Cornelius, Tilia Ellendorff, Fabio Rinaldi:
Approaching SMM4H with auto-regressive language models and back-translation. 146-148 - Atul Kr. Ojha, Priya Rani, Koustava Goswami, Bharathi Raja Chakravarthi, John P. McCrae:
ULD-NUIG at Social Media Mining for Health Applications (#SMM4H) Shared Task 2021. 149-152
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