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Detection of semantic data types is a very crucial task in data science forautomated data cleaning, schema matching, data discovery, semantic data typenormalization and sensitive data identification. Existing methods includeregular expression-based or dictionary lookup-based methods that are not robustto dirty as well unseen data and are limited to a very less number of semanticdata types to predict. Existing Machine Learning methods extract large numberof engineered features from data and build logistic regression, random forestor feedforward neural network for this purpose. In this paper, we introduceDCoM, a collection of multi-input NLP-based deep neural networks to detectsemantic data types where instead of extracting large number of features fromthe data, we feed the raw values of columns (or instances) to the model astexts. We train DCoM on 686,765 data columns extracted from VizNet corpus with78 different semantic data types. DCoM outperforms other contemporary resultswith a quite significant margin on the same dataset.

DCoM: A Deep Column Mapper for Semantic Data Type Detection