  Product classification is the task of automatically predicting a taxonomy
path for a product in a predefined taxonomy hierarchy given a textual product
description or title. For efficient product classification we require a
suitable representation for a document (the textual description of a product)
feature vector and efficient and fast algorithms for prediction. To address the
above challenges, we propose a new distributional semantics representation for
document vector formation. We also develop a new two-level ensemble approach
utilizing (with respect to the taxonomy tree) a path-wise, node-wise and
depth-wise classifiers for error reduction in the final product classification.
Our experiments show the effectiveness of the distributional representation and
the ensemble approach on data sets from a leading e-commerce platform and
achieve better results on various evaluation metrics compared to earlier
approaches.
