  A character-level convolutional neural network (CNN) motivated by
applications in "automated machine learning" (AutoML) is proposed to
semantically classify columns in tabular data. Simulated data containing a set
of base classes is first used to learn an initial set of weights. Hand-labeled
data from the CKAN repository is then used in a transfer-learning paradigm to
adapt the initial weights to a more sophisticated representation of the problem
(e.g., including more classes). In doing so, realistic data imperfections are
learned and the set of classes handled can be expanded from the base set with
reduced labeled data and computing power requirements. Results show the
effectiveness and flexibility of this approach in three diverse domains:
semantic classification of tabular data, age prediction from social media
posts, and email spam classification. In addition to providing further evidence
of the effectiveness of transfer learning in natural language processing (NLP),
our experiments suggest that analyzing the semantic structure of language at
the character level without additional metadata---i.e., network structure,
headers, etc.---can produce competitive accuracy for type classification, spam
classification, and social media age prediction. We present our open-source
toolkit SIMON, an acronym for Semantic Inference for the Modeling of
ONtologies, which implements this approach in a user-friendly and
scalable/parallelizable fashion.
