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Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task最新文献

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Identifying professions & occupations in Health-related Social Media using Natural Language Processing 使用自然语言处理识别与健康相关的社交媒体中的专业和职业
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.SMM4H-1.31
Alberto Mesa Murgado, Ana Parras Portillo, Pilar López Úbeda, Maite Martin, Alfonso Ureña-López
{"title":"Identifying professions & occupations in Health-related Social Media using Natural Language Processing","authors":"Alberto Mesa Murgado, Ana Parras Portillo, Pilar López Úbeda, Maite Martin, Alfonso Ureña-López","doi":"10.18653/V1/2021.SMM4H-1.31","DOIUrl":"https://doi.org/10.18653/V1/2021.SMM4H-1.31","url":null,"abstract":"This paper describes the entry of the research group SINAI at SMM4H’s ProfNER task on the identification of professions and occupations in social media related with health. Specifically we have participated in Task 7a: Tweet Binary Classification to determine whether a tweet contains mentions of occupations or not, as well as in Task 7b: NER Offset Detection and Classification aimed at predicting occupations mentions and classify them discriminating by professions and working statuses.","PeriodicalId":378985,"journal":{"name":"Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121797533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language models 任务5:通过集成预训练的语言模型对COVID-19潜在病例的自我报告推文进行分类
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.SMM4H-1.25
Ying Luo, L. Pereira, Kobayashi Ichiro
{"title":"OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language models","authors":"Ying Luo, L. Pereira, Kobayashi Ichiro","doi":"10.18653/V1/2021.SMM4H-1.25","DOIUrl":"https://doi.org/10.18653/V1/2021.SMM4H-1.25","url":null,"abstract":"Since the outbreak of coronavirus at the end of 2019, there have been numerous studies on coro- navirus in the NLP arena. Meanwhile, Twitter has been a valuable source of news and a pub- lic medium for the conveyance of information and personal expression. This paper describes the system developed by the Ochadai team for the Social Media Mining for Health Appli- cations (SMM4H) 2021 Task 5, which aims to automatically distinguish English tweets that self-report potential cases of COVID-19 from those that do not. We proposed a model ensemble that leverages pre-trained represen- tations from COVID-Twitter-BERT (Müller et al., 2020), RoBERTa (Liu et al., 2019), and Twitter-RoBERTa (Glazkova et al., 2021). Our model obtained F1-scores of 76% on the test set in the evaluation phase, and 77.5% in the post-evaluation phase.","PeriodicalId":378985,"journal":{"name":"Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125475973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers 使用RoBERTa变压器对推文自我报告不良妊娠结局和潜在COVID-19病例的分类
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.SMM4H-1.18
Lung-Hao Lee, Man-Chen Hung, Chien-Huan Lu, Chang-Hao Chen, Po-Lei Lee, K. Shyu
{"title":"Classification of Tweets Self-reporting Adverse Pregnancy Outcomes and Potential COVID-19 Cases Using RoBERTa Transformers","authors":"Lung-Hao Lee, Man-Chen Hung, Chien-Huan Lu, Chang-Hao Chen, Po-Lei Lee, K. Shyu","doi":"10.18653/V1/2021.SMM4H-1.18","DOIUrl":"https://doi.org/10.18653/V1/2021.SMM4H-1.18","url":null,"abstract":"This study describes our proposed model design for SMM4H 2021 shared tasks. We fine-tune the language model of RoBERTa transformers and their connecting classifier to complete the classification tasks of tweets for adverse pregnancy outcomes (Task 4) and potential COVID-19 cases (Task 5). The evaluation metric is F1-score of the positive class for both tasks. For Task 4, our best score of 0.93 exceeded the mean score of 0.925. For Task 5, our best of 0.75 exceeded the mean score of 0.745.","PeriodicalId":378985,"journal":{"name":"Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task","volume":"8 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114118207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
ULD-NUIG at Social Media Mining for Health Applications (#SMM4H) Shared Task 2021 社交媒体挖掘健康应用(#SMM4H)共享任务2021
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.SMM4H-1.33
Atul Kr. Ojha, P. Rani, Koustava Goswami, Bharathi Raja Chakravarthi, John P. Mccrae
{"title":"ULD-NUIG at Social Media Mining for Health Applications (#SMM4H) Shared Task 2021","authors":"Atul Kr. Ojha, P. Rani, Koustava Goswami, Bharathi Raja Chakravarthi, John P. Mccrae","doi":"10.18653/V1/2021.SMM4H-1.33","DOIUrl":"https://doi.org/10.18653/V1/2021.SMM4H-1.33","url":null,"abstract":"Social media platforms such as Twitter and Facebook have been utilised for various research studies, from the cohort-level discussion to community-driven approaches to address the challenges in utilizing social media data for health, clinical and biomedical information. Detection of medical jargon’s, named entity recognition, multi-word expression becomes the primary, fundamental steps in solving those challenges. In this paper, we enumerate the ULD-NUIG team’s system, designed as part of Social Media Mining for Health Applications (#SMM4H) Shared Task 2021. The team conducted a series of experiments to explore the challenges of task 6 and task 5. The submitted systems achieve F-1 0.84 and 0.53 score for task 6 and 5 respectively.","PeriodicalId":378985,"journal":{"name":"Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117033300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
NLP@NISER: Classification of COVID19 tweets containing symptoms NLP@NISER:包含症状的covid - 19推文分类
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.SMM4H-1.19
Deepak Kumar, Nalin Kumar, Subhankar Mishra
{"title":"NLP@NISER: Classification of COVID19 tweets containing symptoms","authors":"Deepak Kumar, Nalin Kumar, Subhankar Mishra","doi":"10.18653/V1/2021.SMM4H-1.19","DOIUrl":"https://doi.org/10.18653/V1/2021.SMM4H-1.19","url":null,"abstract":"In this paper, we describe our approaches for task six of Social Media Mining for Health Applications (SMM4H) shared task in 2021. The task is to classify twitter tweets containing COVID-19 symptoms in three classes (self-reports, non-personal reports & literature/news mentions). We implemented BERT and XLNet for this text classification task. Best result was achieved by XLNet approach, which is F1 score 0.94, precision 0.9448 and recall 0.94448. This is slightly better than the average score, i.e. F1 score 0.93, precision 0.93235 and recall 0.93235.","PeriodicalId":378985,"journal":{"name":"Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122703309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
KFU NLP Team at SMM4H 2021 Tasks: Cross-lingual and Cross-modal BERT-based Models for Adverse Drug Effects KFU NLP团队在SMM4H 2021任务:跨语言和跨模式基于bert的药物不良反应模型
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.SMM4H-1.6
Andrey Sakhovskiy, Z. Miftahutdinov, E. Tutubalina
{"title":"KFU NLP Team at SMM4H 2021 Tasks: Cross-lingual and Cross-modal BERT-based Models for Adverse Drug Effects","authors":"Andrey Sakhovskiy, Z. Miftahutdinov, E. Tutubalina","doi":"10.18653/V1/2021.SMM4H-1.6","DOIUrl":"https://doi.org/10.18653/V1/2021.SMM4H-1.6","url":null,"abstract":"This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2021 Shared Task. We participated in two tasks on classification of tweets that mention an adverse drug effect (ADE) (Tasks 1a & 2) and two tasks on extraction of ADE concepts (Tasks 1b & 1c). For classification, we investigate the impact of joint use of BERTbased language models and drug embeddings obtained by chemical structure BERT-based encoder. The BERT-based multimodal models ranked first and second on classification of Russian (Task 2) and English tweets (Task 1a) with the F1 scores of 57% and 61%, respectively. For Task 1b and 1c, we utilized the previous year’s best solution based on the EnDR-BERT model with additional corpora. Our model achieved the best results in Task 1c, obtaining an F1 of 29%.","PeriodicalId":378985,"journal":{"name":"Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132975876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 10
Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021 naacl2021第六届健康应用社交媒体挖掘(#SMM4H)共享任务概述
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.SMM4H-1.4
A. Magge, A. Klein, Antonio Miranda-Escalada, M. Ali Al-Garadi, I. Alimova, Z. Miftahutdinov, Eulàlia Farré, Salvador Lima López, Ivan Flores, K. O’Connor, D. Weissenbacher, E. Tutubalina, A. Sarker, J. Banda, Martin Krallinger, G. Gonzalez-Hernandez
{"title":"Overview of the Sixth Social Media Mining for Health Applications (#SMM4H) Shared Tasks at NAACL 2021","authors":"A. Magge, A. Klein, Antonio Miranda-Escalada, M. Ali Al-Garadi, I. Alimova, Z. Miftahutdinov, Eulàlia Farré, Salvador Lima López, Ivan Flores, K. O’Connor, D. Weissenbacher, E. Tutubalina, A. Sarker, J. Banda, Martin Krallinger, G. Gonzalez-Hernandez","doi":"10.18653/V1/2021.SMM4H-1.4","DOIUrl":"https://doi.org/10.18653/V1/2021.SMM4H-1.4","url":null,"abstract":"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. The Social Media Mining for Health Applications (#SMM4H) shared tasks in its sixth iteration sought to advance the use of social media texts such as Twitter for pharmacovigilance, disease tracking and patient centered outcomes. #SMM4H 2021 hosted a total of eight tasks that included reruns of adverse drug effect extraction in English and Russian and newer tasks such as detecting medication non-adherence from Twitter and WebMD forum, detecting self-reported adverse pregnancy outcomes, detecting cases and symptoms of COVID-19, identifying occupations mentioned in Spanish by Twitter users, and detecting self-reported breast cancer diagnosis. The eight tasks included a total of 12 individual subtasks spanning three languages requiring methods for binary classification, multi-class classification, named entity recognition and entity normalization. With a total of 97 registering teams and 40 teams submitting predictions, the interest in the shared tasks grew by 70% and participation grew by 38% compared to the previous iteration.","PeriodicalId":378985,"journal":{"name":"Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128322639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 70
Fine-tuning BERT to classify COVID19 tweets containing symptoms 微调BERT对包含症状的covid - 19推文进行分类
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.SMM4H-1.30
Rajarshi Roychoudhury, S. Naskar
{"title":"Fine-tuning BERT to classify COVID19 tweets containing symptoms","authors":"Rajarshi Roychoudhury, S. Naskar","doi":"10.18653/V1/2021.SMM4H-1.30","DOIUrl":"https://doi.org/10.18653/V1/2021.SMM4H-1.30","url":null,"abstract":"Twitter is a valuable source of patient-generated data that has been used in various population health studies. The first step in many of these studies is to identify and capture Twitter messages (tweets) containing medication mentions. Identifying personal mentions of COVID19 symptoms requires distinguishing personal mentions from other mentions such as symptoms reported by others and references to news articles or other sources. In this article, we describe our submission to Task 6 of the Social Media Mining for Health Applications (SMM4H) Shared Task 2021. This task challenged participants to classify tweets where the target classes are:(1) self-reports,(2) non-personal reports, and (3) literature/news mentions. Our system used a handcrafted preprocessing and word embeddings from BERT encoder model. We achieved an F1 score of 93%","PeriodicalId":378985,"journal":{"name":"Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114760780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Pre-trained Transformer-based Classification and Span Detection Models for Social Media Health Applications 社交媒体健康应用中基于预训练变压器的分类和跨度检测模型
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.SMM4H-1.8
Yuting Guo, Y. Ge, M. Ali Al-Garadi, A. Sarker
{"title":"Pre-trained Transformer-based Classification and Span Detection Models for Social Media Health Applications","authors":"Yuting Guo, Y. Ge, M. Ali Al-Garadi, A. Sarker","doi":"10.18653/V1/2021.SMM4H-1.8","DOIUrl":"https://doi.org/10.18653/V1/2021.SMM4H-1.8","url":null,"abstract":"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. We developed two separate systems for classification and span detection, both based on pre-trained Transformer-based models. In addition, we applied oversampling and classifier ensembling in the classification tasks. The results of our submissions are over the median scores in all tasks except for Task 1a. Furthermore, our model achieved first place in Task 4 and obtained a 7% higher F1-score than the median in Task 1b.","PeriodicalId":378985,"journal":{"name":"Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task","volume":"768 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123281864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
UACH-INAOE at SMM4H: a BERT based approach for classification of COVID-19 Twitter posts UACH-INAOE在SMM4H:基于BERT的COVID-19推特帖子分类方法
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task Pub Date : 2021-06-01 DOI: 10.18653/V1/2021.SMM4H-1.10
A. Valdés, J. López, M. Montes
{"title":"UACH-INAOE at SMM4H: a BERT based approach for classification of COVID-19 Twitter posts","authors":"A. Valdés, J. López, M. Montes","doi":"10.18653/V1/2021.SMM4H-1.10","DOIUrl":"https://doi.org/10.18653/V1/2021.SMM4H-1.10","url":null,"abstract":"This work describes the participation of the Universidad Autónoma de Chihuahua - Instituto Nacional de Astrofísica, Óptica y Electrónica team at the Social Media Mining for Health Applications (SMM4H) 2021 shared task. Our team participated in task 5 and 6, both focused on the automatic classification of Twitter posts related to COVID-19. Task 5 was oriented on solving a binary classification problem, trying to identify self-reporting tweets of potential cases of COVID-19. Task 6 objective was to classify tweets containing COVID-19 symptoms. For both tasks we used models based on bidirectional encoder representations from transformers (BERT). Our objective was to determine if a model pretrained on a corpus in the domain of interest can outperform one trained on a much larger general domain corpus. Our F1 results were encouraging, 0.77 and 0.95 for task 5 and 6 respectively, having achieved the highest score among all the participants in the latter.","PeriodicalId":378985,"journal":{"name":"Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124720186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
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