Inner Mongolia Key Laboratory of Mongolian Information Processing Technology, College of Computer Science, Inner Mongolia Univeristy, University West Road, Hohhot, China.
BMC Med Inform Decis Mak. 2020 Jul 9;20(Suppl 3):125. doi: 10.1186/s12911-020-1122-3.
To provide satisfying answers, medical QA system has to understand the intentions of the users' questions precisely. For medical intent classification, it requires high-quality datasets to train a deep-learning approach in a supervised way. Currently, there is no public dataset for Chinese medical intent classification, and the datasets of other fields are not applicable to the medical QA system. To solve this problem, we construct a Chinese medical intent dataset (CMID) using the questions from medical QA websites. On this basis, we compare four intent classification models on CMID using a case study.
The questions in CMID are obtained from several medical QA websites. The intent annotation standard is developed by the medical experts, which includes four types and 36 subtypes of users' intents. Besides the intent label, CMID also provides two types of additional information, including word segmentation and named entity. We use the crowdsourcing way to annotate the intent information for each Chinese medical question. Word segmentation and named entities are obtained using the Jieba and a well-trained Lattice-LSTM model. We loaded a Chinese medical dictionary consisting of 530,000 for word segmentation to obtain a more accurate result. We also select four popular deep learning-based models and compare their performances of intent classification on CMID.
The final CMID contains 12,000 Chinese medical questions and is organized in JSON format. Each question is labeled the intention, word segmentation, and named entity information. The information about question length, number of entities, and are also detailed analyzed. Among Fast Text, TextCNN, TextRNN, and TextGCN, Fast Text and TextCNN models have achieved the best results in four types and 36 subtypes intent classification, respectively.
In this work, we provide a dataset for Chinese medical intent classification, which can be used in medical QA and related fields. We performed an intent classification task on the CMID. In addition, we also did some analysis on the content of the dataset.
为了提供令人满意的答案，医学 QA 系统必须准确理解用户问题的意图。对于医学意图分类，它需要高质量的数据集来以监督的方式训练深度学习方法。目前，没有用于中文医学意图分类的公共数据集，而其他领域的数据集不适用于医学 QA 系统。为了解决这个问题，我们使用来自医学 QA 网站的问题构建了一个中文医学意图数据集（CMID）。在此基础上，我们通过案例研究比较了四种意图分类模型在 CMID 上的表现。
CMID 中的问题是从几个医学 QA 网站中获取的。意图标注标准是由医学专家制定的，包括用户意图的四个类型和 36 个子类型。除了意图标签外，CMID 还提供了两种类型的附加信息，包括分词和命名实体。我们使用众包方式为每个中文医学问题标注意图信息。分词和命名实体是使用结巴和训练有素的 Lattice-LSTM 模型获得的。我们加载了一个包含 53 万个单词的中文医学词典，以获得更准确的结果。我们还选择了四个流行的基于深度学习的模型，并比较了它们在 CMID 上的意图分类性能。
最终的 CMID 包含 12000 个中文医学问题，以 JSON 格式组织。每个问题都标注了意图、分词和命名实体信息。还详细分析了问题长度、实体数量等信息。在 FastText、TextCNN、TextRNN 和 TextGCN 中，FastText 和 TextCNN 模型在四类和三十六种子类意图分类中分别取得了最好的结果。
在这项工作中，我们提供了一个用于中文医学意图分类的数据集，可用于医学 QA 和相关领域。我们在 CMID 上执行了意图分类任务。此外，我们还对数据集的内容进行了一些分析。
