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。

CLaCLab 在 SocialDisNER 的应用：使用医学词表识别西班牙语推文中的疾病实体

CLaCLab at SocialDisNER: Using Medical Gazetteers for Named-Entity Recognition of Disease Mentions in Spanish Tweets

In this article we describe our participation in TASS 2019, a shared task
aimed at the detection of sentiment polarity of Spanish tweets. We combined
different representations such as bag-of-words, bag-of-characters, and tweet
embeddings. In particular, we trained robust subword-aware word embeddings and
computed tweet representations using a weighted-averaging strategy. We also
used two data augmentation techniques to deal with data scarcity: two-way
translation augmentation, and instance crossover augmentation, a novel
technique that generates new instances by combining halves of tweets. In
experiments, we trained linear classifiers and ensemble models, obtaining
highly competitive results despite the simplicity of our approaches.

文章介绍了作者参加 TASS 2019 的经历，用了诸如字符包袋、词包袋、推文嵌入的表示方法。文章也使用了两种数据增强技术，最后得出简单方法的高竞争实验结果。