MASON-NLP 在 eRisk 2023 上的工作：基于深度学习的社交媒体文本抑郁症状检测
MASON-NLP at eRisk 2023: Deep Learning-Based Detection of Depression Symptoms from Social Media Texts
AI总结：
本研究采用结合MentalBERT、RoBERTa和LSTM的深度学习方法，参与eRisk 2023任务1，旨在从社交媒体文本中识别与BDI问卷抑郁症状相关的句子，尽管结果低于预期，但揭示了该任务的挑战性。
摘要：
抑郁症是一种对人们生活产生深远影响的心理健康障碍。近期研究表明，抑郁症的迹象可以通过个体的交流方式（包括口头语言和书面文本）被检测到。特别是，社交媒体帖子是一种丰富且便捷的文本来源，我们可以从中检查抑郁症状。贝克抑郁量表（BDI）问卷是常用于衡量抑郁症严重程度的工具之一，它可以辅助这项研究。由于每个BDI问题都与特定的抑郁症状相关联，我们可以将研究范围缩小到这些症状上。需要注意的是，并非所有抑郁症患者都会同时表现出所有症状，而是表现出其中的组合。因此，能够判断一个句子或一段用户生成内容是否与某一特定状况相关，是极为有用的。基于此，eRisk 2023 任务1被设计为正是为了达成这一目标：评估不同句子与BDI问卷中列出的抑郁症状的相关性。本报告主要介绍我们的团队Mason-NLP如何参与这一子任务，该任务涉及识别与不同抑郁症状相关的句子。我们采用了一种结合MentalBERT、RoBERTa和LSTM的深度学习方法。尽管我们付出了努力，评估结果仍低于预期，这凸显了从关于抑郁症的大量数据集中对句子进行排序所固有的挑战，这既需要适当的方法选择，也需要大量的计算资源。我们预计，随着我们的理解和技术的不断发展，未来该共享任务的迭代将产生更好的结果。
英文摘要：
Depression is a mental health disorder that has a profound impact on people's lives. Recent research suggests that signs of depression can be detected in the way individuals communicate, both through spoken words and written texts. In particular, social media posts are a rich and convenient text source that we may examine for depressive symptoms. The Beck Depression Inventory (BDI) Questionnaire, which is frequently used to gauge the severity of depression, is one instrument that can aid in this study. We can narrow our study to only those symptoms since each BDI question is linked to a particular depressive symptom. It's important to remember that not everyone with depression exhibits all symptoms at once, but rather a combination of them. Therefore, it is extremely useful to be able to determine if a sentence or a piece of user-generated content is pertinent to a certain condition. With this in mind, the eRisk 2023 Task 1 was designed to do exactly that: assess the relevance of different sentences to the symptoms of depression as outlined in the BDI questionnaire. This report is all about how our team, Mason-NLP, participated in this subtask, which involved identifying sentences related to different depression symptoms. We used a deep learning approach that incorporated MentalBERT, RoBERTa, and LSTM. Despite our efforts, the evaluation results were lower than expected, underscoring the challenges inherent in ranking sentences from an extensive dataset about depression, which necessitates both appropriate methodological choices and significant computational resources. We anticipate that future iterations of this shared task will yield improved results as our understanding and techniques evolve.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
