Scarcity of data and technological limitations for resource-poor languages in developing countries like India poses a threat to the development of sophisticated NLU systems for healthcare. To assess the current status of various state-of-the-art language models in healthcare, this paper studies the problem by initially proposing two different Healthcare datasets, Indian Healthcare Query Intent-WebMD and 1mg (IHQID-WebMD and IHQID-1mg) and one real world Indian hospital query data in English and multiple Indic languages (Hindi, Bengali, Tamil, Telugu, Marathi and Gujarati) which are annotated with the query intents as well as entities. Our aim is to detect query intents and extract corresponding entities. We perform extensive experiments on a set of models in various realistic settings and explore two scenarios based on the access to English data only (less costly) and access to target language data (more expensive). We analyze context specific practical relevancy through empirical analysis. The results, expressed in terms of overall F1 score show that our approach is practically useful to identify intents and entities.

该研究旨在评估印度及其他资源匮乏开发中国家在医疗保健中自然语言处理系统发展方面所面临的数据稀缺和技术限制问题。作者使用印度医疗保健查询意图数据集、印度医院查询数据及英语和印度语种注释，探讨利用英语和目标语言数据测试多个模型场景下的查询意图和实体提取方法的实用性。结果显示，作者提出的方法可以快速准确地识别意图和实体。

印度语中医疗查询意图识别与实体提取