This paper presents our submission for the SMM4H 2022-Shared Task on the
classification of self-reported intimate partner violence on Twitter (in
English). The goal of this task was to accurately determine if the contents of
a given tweet demonstrated someone reporting their own experience with intimate
partner violence. The submitted system is an ensemble of five RoBERTa models
each weighted by their respective F1-scores on the validation data-set. This
system performed 13% better than the baseline and was the best performing
system overall for this shared task.

该论文介绍了我们在 SMM4H 2022 共享任务中对 Twitter 上的自我报告的亲密伴侣暴力进行分类的提交。该系统是由五个 RoBERTa 模型的集成组成，并通过其在验证数据集上的 F1 分数进行加权。这个系统的效果比基线提高了 13％，并成为这个共享任务中表现最好的系统。

使用多个 BERT 模型在推特中对自报家庭暴力进行分类

AIR-JPMC@SMM4H'22: Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple BERT-based Models

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.

该论文总结了 CLaC 团队对 SMM4H2022 任务 10 的研究，该任务涉及识别西班牙推文中提到的疾病。在将每个标记进行分类之前，该团队使用多语言 RoBERTa Large、UMLS 名录和 DISTEMIST 名录等特征对每个标记进行编码，最终获得了严格的 F1 得分为 0.869，与竞争平均值 0.675，标准偏差 0.245 和中级 0.761。