医疗问答中纠错能显著提升检索效果，尤其需修正用户查询。
Spelling Correction in Healthcare Query-Answer Systems: Methods, Retrieval Impact, and Empirical Evaluation
- 在真实医疗查询上测试四种拼写纠错方法，评估其对检索的影响。
- 使用编辑距离和上下文感知方法，平均召回率提升9.2%，相关性指标提升8.3%。
- 实验证明仅修正语料库无效，必须修正用户查询才有效。
医疗问答系统面临用户输入拼写错误率远高于专业文档的问题。本文首次在真实消费者查询上开展拼写纠错作为检索预处理的受控研究。我们在TREC 2017 LiveQA Medical数据集（104个健康问题）和HealthSearchQA（4,436条来自Google自动补全的健康查询）上进行错误普查，发现61.5%的真实医疗查询至少含一个拼写错误，词级错误率为11.0%。我们评估了四种纠错方法：保守编辑距离、标准编辑距离（Levenshtein）、上下文感知候选排序和SymSpell，涵盖三种实验条件：未纠正查询+未纠正语料库（基线）、未纠正查询+纠正语料库、完全纠正查询+纠正语料库。基于BM25和TF-IDF余弦相似度，在1,935个MedQuAD答案段落与TREC相关性判断下，查询纠错显著提升检索效果：编辑距离和上下文感知方法分别使MRR提高+9.2%，NDCG@10提高+8.3%。关键发现：仅纠正语料库时，MRR仅提升+0.5%，证实查询端纠错才是核心。我们还对100个样本进行错误分析，分类各方法效果，为实践者提供证据支持的建议。
原文摘要 · Abstract (English)
Healthcare question-answering (QA) systems face a persistent challenge: users submit queries with spelling errors at rates substantially higher than those found in the professional documents they search. This paper presents the first controlled study of spelling correction as a retrieval preprocessing step in healthcare QA using real consumer queries. We conduct an error census across two public datasets -- the TREC 2017 LiveQA Medical track (104 consumer health questions) and HealthSearchQA (4,436 health queries from Google autocomplete) -- finding that 61.5% of real medical queries contain at least one spelling error, with a token-level error rate of 11.0%. We evaluate four correction methods -- conservative edit distance, standard edit distance (Levenshtein), context-aware candidate ranking, and SymSpell -- across three experimental conditions: uncorrected queries against an uncorrected corpus (baseline), uncorrected queries against a corrected corpus, and fully corrected queries against a corrected corpus. Using BM25 and TF-IDF cosine retrieval over 1,935 MedQuAD answer passages with TREC relevance judgments, we find that query correction substantially improves retrieval -- edit distance and context-aware correction achieve MRR improvements of +9.2% and NDCG@10 improvements of +8.3% over the uncorrected baseline. Critically, correcting only the corpus without correcting queries yields minimal improvement (+0.5% MRR), confirming that query-side correction is the key intervention. We complement these results with a 100-sample error analysis categorising correction outcomes per method and provide evidence-based recommendations for practitioners.
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