  Chinese Spelling Correction (CSC) is gaining increasing attention due to its
promise of automatically detecting and correcting spelling errors in Chinese
texts. Despite its extensive use in many applications, like search engines and
optical character recognition systems, little has been explored in medical
scenarios in which complex and uncommon medical entities are easily misspelled.
Correcting the misspellings of medical entities is arguably more difficult than
those in the open domain due to its requirements of specificdomain knowledge.
In this work, we define the task of Medical-domain Chinese Spelling Correction
and propose MCSCSet, a large scale specialist-annotated dataset that contains
about 200k samples. In contrast to the existing open-domain CSC datasets,
MCSCSet involves: i) extensive real-world medical queries collected from
Tencent Yidian, ii) corresponding misspelled sentences manually annotated by
medical specialists. To ensure automated dataset curation, MCSCSet further
offers a medical confusion set consisting of the commonly misspelled characters
of given Chinese medical terms. This enables one to create the medical
misspelling dataset automatically. Extensive empirical studies have shown
significant performance gaps between the open-domain and medical-domain
spelling correction, highlighting the need to develop high-quality datasets
that allow for Chinese spelling correction in specific domains. Moreover, our
work benchmarks several representative Chinese spelling correction models,
establishing baselines for future work.
