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Data cleaning
More data is good, but clean data is always better. Cleaned and correctly processed data is what makes the difference. Clean data can mean different things, ranging from removing data bias to assuring better linguistic quality. Or filtering data to perform specific customized training. We can help you do the most with less but highly clean data.
What does our data cleaning process look like?
10 steps to clean data
Mandatory
Tokenization
Deduplication
Language Identification
Heuristic Rules
Advanced Models
Recommended
Custom Filtering
Anonymization
HLP Actions
Optional
Clustering and Domain Filtering
Human Evaluation
Data cleaning as a data quality solution
Improve language quality
Data cleaning has an immediate and measurable impact on the output quality of MT engines from a purely linguistic perspective.
Remove data bias
Data cleaning may also be applied to remove data bias and filter legacy data from outdated cultural annotations or salutations.
Customized training
MT engines may need to be adapted for special use cases, like customer support or product upgrades. In these cases, data cleaning can help filter data based on grammatical categories, tags or out-of-domain texts. Custom corpora can be built based on shorter sentence length, specific keywords and vocabulary.
Upgrade your Data
Partner with our NLP experts to clean and enhance your existing data to achieve optimal ML results.