检测泰国博客帖子中的抑郁症：数据集和基线
Detecting Depression in Thai Blog Posts: a Dataset and a Baseline
摘要：
我们提出了第一个公开可用的语料库检测抑郁症在泰国。我们的语料库是由几个在线博客中经过专家验证的抑郁症病例汇编而成的。我们使用两种不同的基于LSTM的模型和两种不同的基于BERT的模型进行实验。我们达到了77.53%的准确性与泰国BERT模型在检测抑郁症。这为未来的研究人员在同一语料库上建立了一个很好的基线。此外，我们确定了一个需要泰国的嵌入，已经训练了一个更多样化的语料库比维基百科。我们的语料库、代码和训练模型已在Zenodo上公开发布。
英文摘要：
We present the first openly available corpus for detecting depression in Thai. Our corpus is compiled by expert verified cases of depression in several online blogs. We experiment with two different LSTM based models and two different BERT based models. We achieve a 77.53\% accuracy with a Thai BERT model in detecting depression. This establishes a good baseline for future researcher on the same corpus. Furthermore, we identify a need for Thai embeddings that have been trained on a more varied corpus than Wikipedia. Our corpus, code and trained models have been released openly on Zenodo.
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。
