pwc-archive/papers-with-abstracts huggingface dataset:
NLP-CIC-WFU at SocialDisNER: Disease Mention Extraction in Spanish Tweets Using Transfer Learning and Search by Propagation
Authors: Antonio Tamayo, Alexander Gelbukh, Diego Burgos
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Transfer Learning

Named entity recognition (e.g., disease mention extraction) is one of the most relevant tasks for data mining in the medical field. Although it is a well-known challenge, the bulk of the efforts to tackle this task have been made using clinical texts commonly written in English. In this work, we present our contribution to the SocialDisNER competition, which consists of a transfer learning approach to extracting disease mentions in a corpus from Twitter written in Spanish. We fine-tuned a model based on mBERT and applied post-processing using regular expressions to propagate the entities identified by the model and enhance disease mention extraction. Our system achieved a competitive strict F1 of 0.851 on the testing data set.
yiriyou@SMM4H’22: Stance and Premise Classification in Domain Specific Tweets with Dual-View Attention Neural Networks
Authors: Huabin Yang, Zhongjian Zhang, Yanru Zhang
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Pseudo Label, Stance Detection, Task 2

The paper introduces the methodology proposed for the shared Task 2 of the Social Media Mining for Health Application (SMM4H) in 2022. Task 2 consists of two subtasks: Stance Detection and Premise Classification, named Subtask 2a and Subtask 2b, respectively. Our proposed system is based on dual-view attention neural networks and achieves an F1 score of 0.618 for Subtask 2a (0.068 more than the median) and an F1 score of 0.630 for Subtask 2b (0.017 less than the median). Further experiments show that the domain-specific pre-trained model, cross-validation, and pseudo-label techniques contribute to the improvement of system performance.
SINAI@SMM4H’22: Transformers for biomedical social media text mining in Spanish
Authors: Mariia Chizhikova, Pilar López-Úbeda, Manuel C. Díaz-Galiano, L. Alfonso Ureña-López, M. Teresa Martín-Valdivia
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

This paper covers participation of the SINAI team in Tasks 5 and 10 of the Social Media Mining for Health (#SSM4H) workshop at COLING-2022. These tasks focus on leveraging Twitter posts written in Spanish for healthcare research. The objective of Task 5 was to classify tweets reporting COVID-19 symptoms, while Task 10 required identifying disease mentions in Twitter posts. The presented systems explore large RoBERTa language models pre-trained on Twitter data in the case of tweet classification task and general-domain data for the disease recognition task. We also present a text pre-processing methodology implemented in both systems and describe an initial weakly-supervised fine-tuning phase alongside with a submission post-processing procedure designed for Task 10. The systems obtained 0.84 F1-score on the Task 5 and 0.77 F1-score on Task 10.
BOUN-TABI@SMM4H’22: Text-to-Text Adverse Drug Event Extraction with Data Balancing and Prompting
Authors: Gökçe Uludoğan, Zeynep Yirmibeşoğlu
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Event Extraction

This paper describes models developed for the Social Media Mining for Health 2022 Shared Task. We participated in two subtasks: classification of English tweets reporting adverse drug events (ADE) (Task 1a) and extraction of ADE spans in such tweets (Task 1b). We developed two separate systems based on the T5 model, viewing these tasks as sequence-to-sequence problems. To address the class imbalance, we made use of data balancing via over- and undersampling on both tasks. For the ADE extraction task, we explored prompting to further benefit from the T5 model and its formulation. Additionally, we built an ensemble model, utilizing both balanced and prompted models. The proposed models outperformed the current state-of-the-art, with an F1 score of 0.655 on ADE classification and a Partial F1 score of 0.527 on ADE extraction.
uestcc@SMM4H’22: RoBERTa based Adverse Drug Events Classification on Tweets
Authors: Chunchen Wei, Ran Bi, Yanru Zhang
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

This is a description of our participation in the ADE Mining in English Tweets shared task, organized by the Social Media Mining for Health SMM4H 2022 workshop. We participate in the subtask a of shared Task 1, and the paper introduces the system we developed for solving the task. The task requires classifying the given tweets by whether they mention the Adverse Drug Effects. We utilize RoBERTa model and apply several methods during training and finetuning period. We also try to improve the performance of our system by preprocessing the dataset but improve the precision only. The results of our system on test set are 0.601 in F1- score, 0.705 in precision, and 0.524 in recall.
Zhegu@SMM4H-2022: The Pre-training Tweet & Claim Matching Makes Your Prediction Better
Authors: Pan He, Chen YuZe, Yanru Zhang
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Sentence, Task 2

SMM4H-2022 (CITATION) Task 2 is to detect whether containing premise in the tweets of users about COVID-19 on the social medias or their stances for the claims. In this paper, we propose Tweet Claim Matching (TCM), which is a new pre-training task constructed by the tweets and claims similarly to Next Sentence Prediction (NSP). We first continue to pre-train the standard pre-trained language models on the labelled dataset and then fine-tune them for obtaining better performance. Compared with the solid baseline (CITATION), we achieve the absolute improvement of 7.9% in Task 2a and obtain the SOTA results.
MaNLP@SMM4H’22: BERT for Classification of Twitter Posts
Authors: Keshav Kapur, Rajitha Harikrishnan, Sanjay Singh
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Binary Classification, Classification

The reported work is our straightforward approach for the shared task “Classification of tweets self-reporting age” organized by the “Social Media Mining for Health Applications (SMM4H)” workshop. This literature describes the approach that was used to build a binary classification system, that classifies the tweets related to birthday posts into two classes namely, exact age(positive class) and non-exact age(negative class). We made two submissions with variations in the preprocessing of text which yielded F1 scores of 0.80 and 0.81 when evaluated by the organizers.
John_Snow_Labs@SMM4H’22: Social Media Mining for Health (#SMM4H) with Spark NLP
Authors: Veysel Kocaman, Cabir Celik, Damla Gurbaz, Gursev Pirge, Bunyamin Polat, Halil Saglamlar, Meryem Vildan Sarikaya, Gokhan Turer, David Talby
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Classification, GPU, NER, text-classification, Text Classification, token-classification, Token Classification

Social media has become a major source of information for healthcare professionals but due to the growing volume of data in unstructured format, analyzing these resources accurately has become a challenge. In this study, we trained health related NER and classification models on different datasets published within the Social Media Mining for Health Applications (#SMM4H 2022) workshop. Transformer based Bert for Token Classification and Bert for Sequence Classification algorithms as well as vanilla NER and text classification algorithms from Spark NLP library were utilized during this study without changing the underlying DL architecture. The trained models are available within a production-grade code base as part of the Spark NLP library; can scale up for training and inference in any Spark cluster; has GPU support and libraries for popular programming languages such as Python, R, Scala and Java.
READ-BioMed@SocialDisNER: Adaptation of an Annotation System to Spanish Tweets
Authors: Antonio Jimeno Yepes, Karin Verspoor
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

We describe the work of the READ-BioMed team for the preparation of a submission to the SocialDisNER Disease Named Entity Recognition (NER) Task (Task 10) in 2022. We had developed a system for named entity recognition for identifying biomedical concepts in English MEDLINE citations and Spanish clinical text for the LivingNER 2022 challenge. Minimal adaptation of our system was required to perform named entity recognition in the Spanish tweets in the SocialDisNER task, given the availability of Spanish pre-trained language models and the SocialDisNER training data. Minor additions included treatment of emojis and entities in hashtags and Twitter account names.
PLN CMM at SocialDisNER: Improving Detection of Disease Mentions in Tweets by Using Document-Level Features
Authors: Matias Rojas, Jose Barros, Kinan Martin, Mauricio Araneda-Hernandez, Jocelyn Dunstan
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Language Modeling, Language Modelling, Sentence

This paper describes our approaches used to solve the SocialDisNER task, which belongs to the Social Media Mining for Health Applications (SMM4H) shared task. This task aims to identify disease mentions in tweets written in Spanish. The proposed model is an architecture based on the FLERT approach. It consists of fine-tuning a language model that creates an input representation of a sentence based on its neighboring sentences, thus obtaining the document-level context. The best result was obtained using an ensemble of six language models using the FLERT approach. The system achieved an F1 score of 0.862, significantly surpassing the average performance among competitor models of 0.680 on the test partition.
CIC NLP at SMM4H 2022: a BERT-based approach for classification of social media forum posts
Authors: Atnafu Lambebo Tonja, Olumide Ebenezer Ojo, Mohammed Arif Khan, Abdul Gafar Manuel Meque, Olga Kolesnikova, Grigori Sidorov, Alexander Gelbukh
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Classification

This paper describes our submissions for the Social Media Mining for Health (SMM4H) 2022 shared tasks. We participated in 2 tasks: a) Task 4: Classification of Tweets self-reporting exact age and b) Task 9: Classification of Reddit posts self-reporting exact age. We evaluated the two( BERT and RoBERTa) transformer based models for both tasks. For Task 4 RoBERTa-Large achieved an F1 score of 0.846 on the test set and BERT-Large achieved an F1 score of 0.865 on the test set for Task 9.
NCUEE-NLP@SMM4H’22: Classification of Self-reported Chronic Stress on Twitter Using Ensemble Pre-trained Transformer Models
Authors: Tzu-Mi Lin, Chao-Yi Chen, Yu-Wen Tzeng, Lung-Hao Lee
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

This study describes our proposed system design for the SMM4H 2022 Task 8. We fine-tune the BERT, RoBERTa, ALBERT, XLNet and ELECTRA transformers and their connecting classifiers. Each transformer model is regarded as a standalone method to detect tweets that self-reported chronic stress. The final output classification result is then combined using the majority voting ensemble mechanism. Experimental results indicate that our approach achieved a best F1-score of 0.73 over the positive class.
BioInfo@UAVR@SMM4H’22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models
Authors: Edgar Morais, José Luis Oliveira, Alina Trifan, Olga Fajarda
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Data Augmentation

This paper describes BioInfo@UAVR team’s approach for adressing subtasks 1a and 1b of the Social Media Mining for Health Applications 2022 shared task. These sub-tasks deal with the classification of tweets that contain an Adverse Drug Event mentions and the detection of spans that correspond to those mentions. Our approach relies on transformer-based models, data augmentation, and an external dataset.
FRE at SocialDisNER: Joint Learning of Language Models for Named Entity Recognition
Authors: Kendrick Cetina, Nuria García-Santa
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Text Generation

This paper describes our followed methodology for the automatic extraction of disease mentions from tweets in Spanish as part of the SocialDisNER challenge within the 2022 Social Media Mining for Health Applications (SMM4H) Shared Task. We followed a Joint Learning ensemble architecture for the fine-tuning of top performing pre-trained language models in biomedical domain for Named Entity Recognition tasks. We used text generation techniques to augment training data. During practice phase of the challenge our approach showed results of 0.87 F1-Score.
ITAINNOVA at SocialDisNER: A Transformers cocktail for disease identification in social media in Spanish
Authors: Rosa Montañés-Salas, Irene López-Bosque, Luis García-Garcés, Rafael del-Hoyo-Alonso
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: zero-shot-classification, Zero-Shot Learning

ITAINNOVA participates in SocialDisNER with a hybrid system which combines Transformer-based Language Models (LMs) with a custom-built gazetteer for Approximate String Matching (ASM) and dedicated text processing techniques for the social media domain. Additionally, zero-shot classification capabilities have been explored in order to support different parts of the system. An extensive analysis on the interactions of these components has been accomplished, making the system stand out above the mean performance of all the participating teams.
mattica@SMM4H’22: Leveraging sentiment for stance & premise joint learning
Authors: Oscar Lithgow-Serrano, Joseph Cornelius, Fabio Rinaldi, Ljiljana Dolamic
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Language Modeling, Language Modelling, Stance Detection, Task 2

This paper describes our submissions to the Social Media Mining for Health Applications (SMM4H) shared task 2022. Our team (mattica) participated in detecting stances and premises in tweets about health mandates related to COVID-19 (Task 2). Our approach was based on using an in-domain Pretrained Language Model, which we fine-tuned by combining different strategies such as leveraging an additional stance detection dataset through two-stage fine-tuning, joint-learning Stance and Premise detection objectives; and ensembling the sentiment-polarity given by an off-the-shelf fine-tuned model.
KU_ED at SocialDisNER: Extracting Disease Mentions in Tweets Written in Spanish
Authors: Antoine Lain, Wonjin Yoon, Hyunjae Kim, Jaewoo Kang, Ian Simpson
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

This paper describes our system developed for the Social Media Mining for Health (SMM4H) 2022 SocialDisNER task. We used several types of pre-trained language models, which are trained on Spanish biomedical literature or Spanish Tweets. We showed the difference in performance depending on the quality of the tokenization as well as introducing silver standard annotations when training the model. Our model obtained a strict F1 of 80.3% on the test set, which is an improvement of +12.8% F1 (24.6 std) over the average results across all submissions to the SocialDisNER challenge.
CHAAI@SMM4H’22: RoBERTa, GPT-2 and Sampling - An interesting concoction
Authors: Christopher Palmer, Sedigheh Khademi Habibabadi, Muhammad Javed, Gerardo Luis Dimaguila, Jim Buttery
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

This paper describes the approaches to the SMM4H 2022 Shared Tasks that were taken by our team for tasks 1 and 6. Task 6 was the “Classification of tweets which indicate self-reported COVID-19 vaccination status (in English)”. The best test F1 score was 0.82 using a CT-BERT model, which exceeded the median test F1 score of 0.77, and was close to the 0.83 F1 score of the SMM4H baseline model. Task 1 was described as the “Classification, detection and normalization of Adverse Events (AE) mentions in tweets (in English)”. We undertook task 1a, and with a RoBERTa-base model achieved an F1 Score of 0.61 on test data, which exceeded the mean test F1 for the task of 0.56.
IAI @ SocialDisNER : Catch me if you can! Capturing complex disease mentions in tweets
Authors: Aman Sinha, Cristina Garcia Holgado, Marianne Clausel, Matthieu Constant
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: NER

Biomedical NER is an active research area today. Despite the availability of state-of-the-art models for standard NER tasks, their performance degrades on biomedical data due to OOV entities and the challenges encountered in specialized domains. We use Flair-NER framework to investigate the effectiveness of various contextual and static embeddings for NER on Spanish tweets, in particular, to capture complex disease mentions.
UCCNLP@SMM4H’22:Label distribution aware long-tailed learning with post-hoc posterior calibration applied to text classification
Authors: Paul Trust, Provia Kadusabe, Ahmed Zahran, Rosane Minghim, Kizito Omala
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Classification, text-classification, Text Classification

The paper describes our submissions for the Social Media Mining for Health (SMM4H) workshop 2022 shared tasks. We participated in 2 tasks: (1) classification of adverse drug events (ADE) mentions in english tweets (Task-1a) and (2) classification of self-reported intimate partner violence (IPV) on twitter (Task 7). We proposed an approach that uses RoBERTa (A Robustly Optimized BERT Pretraining Approach) fine-tuned with a label distribution-aware margin loss function and post-hoc posterior calibration for robust inference against class imbalance. We achieved a 4% and 1 % increase in performance on IPV and ADE respectively when compared with the traditional fine-tuning strategy with unweighted cross-entropy loss.
HaleLab_NITK@SMM4H’22: Adaptive Learning Model for Effective Detection, Extraction and Normalization of Adverse Drug Events from Social Media Data
Authors: Reshma Unnikrishnan, Sowmya Kamath S, Ananthanarayana V. S.
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Question Answering

This paper describes the techniques designed for detecting, extracting and normalizing adverse events from social data as part of the submission for the Shared task, Task 1-SMM4H’22. We present an adaptive learner mechanism for the foundation model to identify Adverse Drug Event (ADE) tweets. For the detected ADE tweets, a pipeline consisting of a pre-trained question-answering model followed by a fuzzy matching algorithm was leveraged for the span extraction and normalization tasks. The proposed method performed well at detecting ADE tweets, scoring an above-average F1 of 0.567 and 0.172 overlapping F1 for ADE normalization. The model’s performance for the ADE extraction task was lower, with an overlapping F1 of 0.435.
Yet@SMM4H’22: Improved BERT-based classification models with Rdrop and PolyLoss
Authors: Yan Zhuang, Yanru Zhang
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

This paper describes our approach for 11 classification tasks (Task1a, Task2a, Task2b, Task3a, Task3b, Task4, Task5, Task6, Task7, Task8 and Task9) from Social Media Mining for Health (SMM4H) 2022 Shared Tasks. We developed a classification model that incorporated Rdrop to augment data and avoid overfitting, Poly Loss and Focal Loss to alleviate sample imbalance, and pseudo labels to improve model performance. The results of our submissions are over or equal to the median scores in almost all tasks. In addition, our model achieved the highest score in Task4, with a higher 7.8% and 5.3% F1-score than the median scores in Task2b and Task3a respectively.
Fraunhofer FKIE @ SMM4H 2022: System Description for Shared Tasks 2, 4 and 9
Authors: Daniel Claeser, Samantha Kent
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Task 2

We present our results for the shared tasks 2, 4 and 9 at the SMM4H Workshop at COLING 2022 achieved by succesfully fine-tuning pre-trained language models to the downstream tasks. We identify the occurence of code-switching in the test data for task 2 as a possible source of considerable performance degradation on the test set scores. We successfully exploit structural linguistic similarities in the datasets of tasks 4 and 9 for training on joined datasets, scoring first in task 9 and on par with SOTA in task 4.
Fraunhofer SIT@SMM4H’22: Learning to Predict Stances and Premises in Tweets related to COVID-19 Health Orders Using Generative Models
Authors: Raphael Frick, Martin Steinebach
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Sentiment Analysis

This paper describes the system used to predict stances towards health orders and to detect premises in Tweets as part of the Social Media Mining for Health 2022 (SMM4H) shared task. It takes advantage of GPT-2 to generate new labeled data samples which are used together with pre-labeled and unlabeled data to fine-tune an ensemble of GAN-BERT models. First experiments on the validation set yielded good results, although it also revealed that the proposed architecture is more suited for sentiment analysis. The system achieved a score of 0.4258 for the stance and 0.3581 for the premise detection on the test set.
UB Health Miners@SMM4H’22: Exploring Pre-processing Techniques To Classify Tweets Using Transformer Based Pipelines.
Authors: Roshan Khatri, Sougata Saha, Souvik Das, Rohini Srihari
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10
Tasks: Binary Classification, Classification, Multi-class Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Here we discuss our implementation of two tasks in the Social Media Mining for Health Applications (SMM4H) 2022 shared tasks – classification, detection, and normalization of Adverse Events (AE) mentioned in English tweets (Task 1) and classification of English tweets self-reporting exact age (Task 4). We have explored different methods and models for binary classification, multi-class classification and named entity recognition (NER) for these tasks. We have also processed the provided dataset for noise, imbalance, and creative language expression from data. Using diverse NLP methods we classified tweets for mentions of adverse drug effects (ADEs) and self-reporting the exact age in the tweets. Further, extracted reactions from the tweets and normalized these adverse effects to a standard concept ID in the MedDRA vocabulary.
CSECU-DSG@SMM4H’22: Transformer based Unified Approach for Classification of Changes in Medication Treatments in Tweets and WebMD Reviews
Authors: Afrin Sultana, Nihad Karim Chowdhury, Abu Nowshed Chy
Date: 2022-10-01 00:00:00
Proceeding: SMM4H (COLING) 2022 10

Medications play a vital role in medical treatment as medication non-adherence reduces clinical benefit, results in morbidity, and medication wastage. Self-declared changes in drug treatment and their reasons are automatically extracted from tweets and user reviews, helping to determine the effectiveness of drugs and improve treatment care. SMM4H 2022 Task 3 introduced a shared task focusing on the identification of non-persistent patients from tweets and WebMD reviews. In this paper, we present our participation in this task. We propose a neural approach that integrates the strengths of the transformer model, the Long Short-Term Memory (LSTM) model, and the fully connected layer into a unified architecture. Experimental results demonstrate the competitive performance of our system on test data with 61% F1-score on task 3a and 86% F1-score on task 3b. Our proposed neural approach ranked first in task 3b.