UMBC Faculty CollectionUMBC Student CollectionGao, HangOates, Tim2018-09-052018-09-052018-07Hang Gao and Tim Oates. 2018. Large Scale Taxonomy Classification using BiLSTM with Self-Attention. In Proceedings of ACM SIGIR Workshop on eCommerce (SIGIR 2018 eCom Data Challenge). ACM, New York, NY, USA, Article 4, 5 pages. https://doi.org/10.475/123_4123-4567-24-567/08/06.http://hdl.handle.net/11603/11240In 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 end-to-end architecture that does not rely on any hand-craft features, and is regulated by various techniques.5 PAGESen-USThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.taxonomy classificationBiLSTMattentionText