AI 中文总结
针对孟加拉语低资源语言医疗问答系统匮乏的问题，构建含4493组问答对的医疗知识库，采用SVM等模型及多相似度指标，实现F1达95%、人工满意度0.9的孟加拉语医疗问答系统。
AI 中文摘要
医疗问答（QA）系统已成为提供可靠健康信息的重要工具，但针对孟加拉语这类低资源语言的相关研究仍非常匮乏，原因是缺乏适配这些语言的数据集和系统。为解决这一问题，我们推出了BanglaMed-QA，这是一款专为孟加拉语医疗领域设计的稳健问答系统。该系统的开发工作首先是构建结构化医疗知识库，其中包含506种疾病下9个类别的4493组问答对；为提升语义理解能力，我们还提出了领域特定的词根词典和同义词集，并采用词性标注来解决指代消解问题。我们采用了监督机器学习模型，其中支持向量机（SVM）被证实是对问题进行分类的最佳模型；同时应用了余弦、雅卡尔（Jaccard）、BM25和莱文斯坦（Levenshtein）等多种相似度指标，并结合软投票和硬投票方法进行查询匹配。我们从两个方面对该问答系统的性能进行了评估：自动评估中F1分数达到95%，人工满意度评分平均为0.9（满分1.0）。这一结果验证了BanglaMed-QA在缩小孟加拉语使用者医疗信息差距方面的实际应用价值。
英文摘要
Medical question answering (QA) systems have become crucial tools for providing reliable health information. But they remain very unexplored for low-resource languages like Bangla due to limited datasets and systems tailored to these languages. To address this, we introduce BanglaMed-QA, a robust QA system specifically designed for the Bangla medical domain. The process begins with building a structured medical knowledge base that includes 4,493 QA pairs in 9 categories under 506 diseases. To improve semantic comprehension, domain-specific root word dictionaries and synonym sets are proposed, in addition to part-of-speech tagging for anaphora resolution. We adopt supervised machine learning models in which SVM is found to be the best model to categorize questions. Multiple similarity metrics, including cosine, Jaccard, BM25, and Levenshtein, are applied with soft and hard voting methods for query matching. The performance of the QA system has been evaluated in two aspects, with a 95% F1 score in an automated evaluation and an average human satisfaction rating of 0.9 out of 1.0. This validates the real-world application of BanglaMed-QA in closing the healthcare information gap for Bangla speakers.
CommentsAccepted and presented at 3rd International Conference on Big Data, IoT and Machine Learning (BIM 2025)
