Large Scale Taxonomy Classification using BiLSTM with Self-Attention
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Author/Creator
Gao, Hang
Oates, Tim
Author/Creator ORCID
Date
2018-07
Type of Work
Department
Program
Citation of Original Publication
Hang 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_4
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This 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.
Subjects
taxonomy classification
BiLSTM
attention
BiLSTM
attention
Abstract
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 end-to-end architecture that does not rely on any hand-craft features, and is regulated by various techniques.