论文标题
通过社交媒体数据集，以本体驱动的自学驱动的自我判断
Ontology-Driven Self-Supervision for Adverse Childhood Experiences Identification Using Social Media Datasets
论文作者
论文摘要
不利的童年经历（ACE）被定义为在整个儿童期和/或青春期中发生的高度压力和可能创伤的事件或情况的集合。它们已被证明与后来生活中心理健康疾病或其他异常行为的风险增加有关。但是，通过自然语言处理（NLP）从文本数据中识别ACE是具有挑战性的，因为（a）没有NLP准备就绪的本体论； （b）几乎没有用于机器学习的资源，因此需要临床专家的数据注释； （c）域专家和大量文档以支持大型机器学习模型的昂贵注释。在本文中，我们介绍了一种本体驱动的自我监督方法（使用基线NLP结果的自动编码器衍生概念嵌入），以生产公开可用的资源，该资源将支持大规模的机器学习（例如，培训基于培训的变压器基于培训的大语言模型）对社交媒体语料库进行。该资源以及拟议的方法旨在促进社区培训可转移的NLP模型，以在电子健康记录中的临床注释中在诸如NLP之类的低资源场景中有效地浮出水面。该资源包括ACE本体术语，ACE概念嵌入和NLP注释语料库的列表，请访问https://github.com/knowlab/ace-nlp。
Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adolescence. They have been shown to be associated with increased risks of mental health diseases or other abnormal behaviours in later lives. However, the identification of ACEs from textual data with Natural Language Processing (NLP) is challenging because (a) there are no NLP ready ACE ontologies; (b) there are few resources available for machine learning, necessitating the data annotation from clinical experts; (c) costly annotations by domain experts and large number of documents for supporting large machine learning models. In this paper, we present an ontology-driven self-supervised approach (derive concept embeddings using an auto-encoder from baseline NLP results) for producing a publicly available resource that would support large-scale machine learning (e.g., training transformer based large language models) on social media corpus. This resource as well as the proposed approach are aimed to facilitate the community in training transferable NLP models for effectively surfacing ACEs in low-resource scenarios like NLP on clinical notes within Electronic Health Records. The resource including a list of ACE ontology terms, ACE concept embeddings and the NLP annotated corpus is available at https://github.com/knowlab/ACE-NLP.
