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Large Scale Taxonomy Classification using BiLSTM with Self-Attention
[article]
2018
In this paper we present a deep learning model for the task of large scale taxonomy classification, where the model is expected to predict the corresponding category ID path given a product title. The proposed approach relies on a Bidirectional Long Short Term Memory Network (BiLSTM) to capture the context information for each word, followed by a multi-head attention model to aggregate useful information from these words as the final representation of the product title. Our model adopts an
doi:10.13016/m2154ds3x
fatcat:iodmiayasvhrniykf4atml7ib4