Korea, Republic of

In this presentation, we describe our approaches to solving 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.

COLING 2022

Improving Detection of Disease Mentions in Tweets by Using Document-Level Features

named entity recognition

language models

information extraction

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



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**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

workshop paper

This paper summarizes the CLaC submission for SMM4H 2022 Task 10 which concerns the recognition of diseases mentioned in Spanish tweets. Before classifying each token, we encode each token with a transformer encoder using features from Multilingual RoBERTa Large, UMLS gazetteer, and DISTEMIST gazetteer, among others. We obtain a strict F1 score of 0.869, with competition mean of 0.675, standard deviation of 0.245, and median of 0.761.

Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets

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.

A BERT-based approach for classification ofsocial media forum posts

My presentation 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.

Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models

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.

A Transformers cocktail for disease identification in social media in Spanish

This paper describes our submissions to the Social Media Mining for Health Applications (SMM4H) shared task 2022. Our team 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.

Leveraging sentiment for stance & premise joint learning

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.

Extracting Disease Mentions in Tweets Written in Spanish

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.


RoBERTa, GPT-2 and Sampling - An interesting concoction

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.

Catchme if you can! Capturing complex diseasementions in tweets

Social media data is extensively used for various informed decision-making applications in diverse domains like e-business, healthcare, policy making and more. As part of the Shared Task Social Media Mining for Health - SMM4H'22 Task-1a is to classify tweets reporting Adverse Drug Events (ADE). Task 1b is to detect the adverse spans and Task 1c is to normalize ADE mentions with MedDRA terms from tweets data. 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.

Adaptive Learning Model for Effective Detection, Extraction and Normalization of Adverse Drug Events from Social Media Data

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.

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Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets

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