使用语言无关的BERT句子嵌入（LaBSE）检测冗余健康调查问题
Detecting Redundant Health Survey Questions Using Language-agnostic BERT Sentence Embedding (LaBSE)
AI总结：
本研究为促进基于调查的PGHD标准化，收集英韩双语健康调查问题构建STS数据集，对比三种分类器后发现SBERT-LaBSE跨语言语义相似度评估表现最佳，ROC和精确召回曲线下面积超0.99，为跨语言PGHD语义互操作提供支持。
摘要：
本研究旨在计算公开可用的健康调查问题之间的语义相似度，以促进基于调查的个人生成健康数据（PGHD）的标准化。我们从NIH CDE Repository、PROMIS、韩国公共卫生机构和学术出版物中收集了英语和韩语的各类健康调查问题，这些问题来自不同的健康生活日志领域。采用随机问题配对方案生成包含1758个问题对的语义文本相似度（STS）数据集，每对问题的相似度分数由两名人类专家分配。然后使用标记数据集构建了三个分类器：基于词袋（Bag-of-Words）、基于BERT嵌入的SBERT、基于LaBSE嵌入的SBERT。使用传统列联统计对算法进行评估，其中SBERT-LaBSE在跨语言问题相似度评估中表现最佳，受试者工作特征（ROC）曲线和精确召回曲线下面积均超过0.99，且在识别跨语言语义相似度方面有效。SBERT-LaBSE算法在对齐两种语言的语义等价句子方面表现出色，但在捕捉细微差异和保持计算效率方面存在挑战。未来研究应侧重于使用更大的多语言数据集进行测试，以及在健康生活日志领域校准和标准化分数以提高一致性。本研究引入了用于计算两种语言语义相似度的SBERT-LaBSE算法，表明其优于基于BERT的模型和词袋方法，突显其在跨语言障碍改善基于调查的PGHD语义互操作性方面的潜力。
英文摘要：
The goal of this work was to compute the semantic similarity among publicly available health survey questions in order to facilitate the standardization of survey-based Person-Generated Health Data (PGHD). We compiled various health survey questions authored in both English and Korean from the NIH CDE Repository, PROMIS, Korean public health agencies, and academic publications. Questions were drawn from various health lifelog domains. A randomized question pairing scheme was used to generate a Semantic Text Similarity (STS) dataset consisting of 1758 question pairs. Similarity scores between each question pair were assigned by two human experts. The tagged dataset was then used to build three classifiers featuring: Bag-of-Words, SBERT with BERT-based embeddings, and SBRET with LaBSE embeddings. The algorithms were evaluated using traditional contingency statistics. Among the three algorithms, SBERT-LaBSE demonstrated the highest performance in assessing question similarity across both languages, achieving an Area Under the Receiver Operating Characteristic (ROC) and Precision-Recall Curves of over 0.99. Additionally, it proved effective in identifying cross-lingual semantic similarities.The SBERT-LaBSE algorithm excelled at aligning semantically equivalent sentences across both languages but encountered challenges in capturing subtle nuances and maintaining computational efficiency. Future research should focus on testing with larger multilingual datasets and on calibrating and normalizing scores across the health lifelog domains to improve consistency. This study introduces the SBERT-LaBSE algorithm for calculating semantic similarity across two languages, showing it outperforms BERT-based models and the Bag of Words approach, highlighting its potential to improve semantic interoperability of survey-based PGHD across language barriers.
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。
