Spell check is a useful application which involves processing noisy human-generated text. Compared to other languages like English, it is more challenging to detect and correct spelling errors in Chinese because it has more (up to 100k) characters . For Chinese spell check,  using confusion sets narrows the search space and makes finding corrections easier. However, most, if not all, confusion sets used to date are fixed and thus do not include new, evolving error patterns . We propose a scalable approach to adapt confusion sets by exploiting hierarchical character embeddings to (1) obviate the need to handcraft confusion sets, and (2) resolve sparsity issues related to seldom-occurring errors. Our approach establishes new SOTA results in spelling error correction on the 2014 and 2015 Chinese Spelling Correction Bake-off datasets.
Spell check is a useful application which processes noisy human-generated text. Spell check for Chinese poses unresolved problems due to the large number of characters, the sparse distribution of errors, and the dearth of resources with sufficient coverage of heterogeneous and shifting error domains . For Chinese spell check, filtering using confusion sets narrows the search space and makes finding corrections easier. However, most, if not all, confusion sets used to date are fixed and thus do not include new, shifting error domains . We propose a scalable adaptable filter that exploits hierarchical character embeddings to (1) obviate the need to handcraft confusion sets, and (2) resolve sparsity problems related to infrequent errors. Our approach compares favorably with competitive baselines and obtains SOTA results on the 2014 and 2015 Chinese Spelling Check Bake-off datasets.
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involves processing
processes
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Compared to other languages like English, it is more challenging to detect and correct spelling errors in Chinese because it has more (up to 100k) characters
Spell check for Chinese poses unresolved problems due to the large number of characters, the sparse distribution of errors, and the dearth of resources with sufficient coverage of heterogeneous and shifting error domains
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filtering
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evolving error patterns
shifting error domains
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approach to adapt confusion sets by exploiting
adaptable filter that exploits
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issues related to seldom-occurring
problems related to infrequent
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establishes new SOTA results in spelling error correction
compares favorably with competitive baselines and obtains SOTA results
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Correction
Check