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Connecting different text attributes associated with the same entity(conflation) is important in business data analytics since it could help mergetwo different tables in a database to provide a more comprehensive profile ofan entity. However, the conflation task is challenging because two text stringsthat describe the same entity could be quite different from each other forreasons such as misspelling. It is therefore critical to develop a conflationmodel that is able to truly understand the semantic meaning of the strings andmatch them at the semantic level. To this end, we develop a character-leveldeep conflation model that encodes the input text strings from character levelinto finite dimension feature vectors, which are then used to compute thecosine similarity between the text strings. The model is trained in anend-to-end manner using back propagation and stochastic gradient descent tomaximize the likelihood of the correct association. Specifically, we proposetwo variants of the deep conflation model, based on long-short-term memory(LSTM) recurrent neural network (RNN) and convolutional neural network (CNN),respectively. Both models perform well on a real-world business analyticsdataset and significantly outperform the baseline bag-of-character (BoC) model.

Character-level Deep Conflation for Business Data Analytics