Korea, Republic of

We propose adversarial methods for increasing the robustness of disease mention detection on social media. Our method applies adversarial data augmentation on the input and the embedding spaces to the English BioBERT model. We evaluate our method in the SocialDisNER challenge at SMM4H&#39;22 on an annotated dataset of disease mentions in Spanish tweets. We find that both methods outperform a heuristic vocabulary-based baseline by a large margin. Additionally, utilizing the English BioBERT model shows a strong performance and outperforms the data augmentation methods even when applied to the Spanish dataset, which has a large amount of data, while augmentation methods show a significant advantage in a low-data setting.

COLING 2022

Robust Cross-Lingual Detection of Disease Mentionson Social Media with Adversarial Methods

adversarial methods

disease detection

biobert

data augmentation

[![](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.

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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.

We propose adversarial methods for increasing the robustness of disease mention detection on social media. Our method applies adversarial data augmentation on the input and the embedding spaces to the English BioBERT model. We evaluate our method in the SocialDisNER challenge at SMM4H'22 on an annotated dataset of disease mentions in Spanish tweets. We find that both methods outperform a heuristic vocabulary-based baseline by a large margin. Additionally, utilizing the English BioBERT model shows a strong performance and outperforms the data augmentation methods even when applied to the Spanish dataset, which has a large amount of data, while augmentation methods show a significant advantage in a low-data setting.

workshop paper

We describe in this paper our proposed systems for the Social Media Mining for Health 2022 shared task 1. In particular, we participated in the three sub-tasks, tasks that aim at extracting and processing Adverse Drug Events.
We investigate different transformer-based pretrained models we fine-tuned on each task and proposed some improvement on the task of entity normalization.

Mining Advent Drug Events Using Pretrained Language Models

There is a pressing need to exploit health-related content from social media, a global source of data where key health information is posted directly by citizens, patients and other healthcare stakeholders. Use cases of disease-related social media mining include disease outbreak/surveillance, mental health and pharmacovigilance. Current efforts address the exploitation of social media beyond English. The SocialDisNER task, organized as part of the SMM4H 2022 initiative, has applied the LINKAGE methodology to select and annotate a Gold Standard corpus of 9,500 tweets in Spanish enriched with disease mentions generated by patients and medical professionals. As a complementary resource for teams participating in the SocialDisNER track, we have also created a large-scale corpus of 85,000 tweets, where in addition to disease mentions, other medical entities of relevance (e.g., medications, symptoms and procedures, among others) have been automatically labelled. Using these large-scale datasets, co-mention networks or knowledge graphs were released for each entity pair type. Out of the 47 teams registered for the task, 17 teams uploaded a total of 32 runs. The top-performing team achieved a very competitive 0.891 f-score, with a system trained following a continue pre-training strategy. We anticipate that the corpus and systems resulting from the SocialDisNER track might further foster health-related text mining of social media content in Spanish and inspire disease detection strategies in other languages.

The SocialDisNER shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines andcorpora

This paper introduces a manually annotated dataset for named entity recognition (NER) in micro-blogging text for the Romanian language. It contains gold annotations for 9 entity classes and expressions: persons, locations, organizations, time expressions, legal references, disorders, chemicals, medical devices and anatomical parts. Furthermore, word embedding models computed on a larger micro-blogging corpus are made available. Finally, several NER models are trained and their performance is evaluated against the newly introduced corpus.

Romanian micro-blogging named entity recognition including health-related entities

This paper describes our submission to Social Media Mining for Health (SMM4H) 2022 Shared Task 8, aimed at detecting self-reported chronic stress on Twitter. Our approach leverages a pre-trained transformer model (RoBERTa) in combination with a Bidirectional Long Short-Term Memory (BiLSTM) network trained on a diverse set of psycholinguistic features. We handle the class imbalance issue in the training dataset by augmenting it by another dataset used for stress classification in social media.

Pre-Trained Language Models Meet a Suite of Psycholinguistic Features for the Detection of Self-Reported Chronic Stress

This is an organizers’ report of the competition on argument mining systems dealing with English tweets about COVID-19 health mandates. This competition was held within the framework of the SMM4H 2022 Workshop. During the competition, the participants were offered two subtasks: stance detection and premise classification. We present a manually annotated corpus containing 6,156 short posts from Twitter on three topics related to the COVID-19 pandemic: school closures, stay-at-home orders, and wearing masks. We hope the prepared dataset will support further research on argument mining in the health field

SMM4H Task 2: Stance and premise detection in tweets about COVID-19 health mandates

Named entity recognition (e.g., disease mention extraction) is one of the most relevant tasks for data mining in the medical field. Our contribution to this task 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. Our system achieved a competitive strict F1 of 0.851 on the testing data set.

Disease Mention Extraction in Spanish Tweets Using Transfer Learning and Search by Propagation

Introduction of the methodology proposed for the shared Task 2 of the Social Media
Mining for Health Application (SMM4H) in 2022

A solution to SMMH Task 2

This presentation covers participation of the SINAI team in the Task 10 of the Social Media Mining for Health (\#SSM4H) workshop at COLING-2022. This task focuses on leveraging Twitter posts written in Spanish for healthcare research. The objective to identify disease mentions in Twitter posts. The presented system explores large RoBERTa language model on general-domain data for the disease recognition task. We also present a text pre-processing methodology and describe an initial weakly-supervised fine-tuning phase alongside with a submission post-processing procedure. The system obtained 0.77 F1-score during the official evaluation.

Weakly Supervised fine-tuning for Disease Mention Detection in Spanish Tweets

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

The Visual Dialogue (VD) task has recently received increasing attention in AI research. VD aims to generate multi-round, interactive responses based on the dialog history and image content. Existing textual dialogue models cannot fully understand visual information, resulting in a lack of scene features when communicating with humans continuously. Therefore, how to efficiently fuse multi-modal data features remains to be a challenge. In this work, we propose a knowledge transfer method with visual prompt (\textbf{VPTG}) fusing multi-modal data, which is a flexible module that can utilize the text-only seq2seq model to handle VD tasks. The VPTG conducts text-image co-learning and multi-modal information fusion with visual prompts and visual knowledge distillation. Specifically, we construct visual prompts from visual representations and then induce sequence-to-sequence (seq2seq) models to fuse visual information and textual contexts by visual-text patterns. Moreover, we also realize visual knowledge transfer through distillation between two different models' text representations, so that the seq2seq model can actively learn visual semantic representations. Extensive experiments on the multi-modal dialogue understanding and generation (MDUG) datasets show the proposed VPTG outperforms other single-modal methods, which demonstrate the effectiveness of visual prompt and visual knowledge transfer.

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