In this paper, we present a customizable datacentric system that
automatically generates common misspellings for complex health-related terms.
The spelling variant generator relies on a dense vector model learned from
large unlabeled text, which is used to find semantically close terms to the
original/seed keyword, followed by the filtering of terms that are lexically
dissimilar beyond a given threshold. The process is executed recursively,
converging when no new terms similar (lexically and semantically) to the seed
keyword are found. Weighting of intra-word character sequence similarities
allows further problem-specific customization of the system. On a dataset
prepared for this study, our system outperforms the current state-of-the-art
for medication name variant generation with best F1-score of 0.69 and
F1/4-score of 0.78. Extrinsic evaluation of the system on a set of
cancer-related terms showed an increase of over 67% in retrieval rate from
Twitter posts when the generated variants are included. Our proposed spelling
variant generator has several advantages over the current state-of-the-art and
other types of variant generators-(i) it is capable of filtering out lexically
similar but semantically dissimilar terms, (ii) the number of variants
generated is low as many low-frequency and ambiguous misspellings are filtered
out, and (iii) the system is fully automatic, customizable and easily
executable. While the base system is fully unsupervised, we show how
supervision maybe employed to adjust weights for task-specific customization.
The performance and significant relative simplicity of our proposed approach
makes it a much needed misspelling generation resource for health-related text
mining from noisy sources. The source code for the system has been made
publicly available for research purposes.

本研究提出了一种可定制化的数据中心系统，能够自动生成复杂健康相关词汇的常见拼写错误，其拼写变量生成器依赖于从大规模未标记的文本中学习的密集向量模型，能够在过滤词汇时递归执行，生成的变体可用于在噪声环境下进行健康文本挖掘。