Large Scale Taxonomy Classification using BiLSTM with Self-Attention

Thumbnail Image

Files

ecom18DC_paper_10.pdf (578.48 KB)

Links to Files

https://doi.org/10.475/123_4

Permanent Link

http://hdl.handle.net/11603/11240

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

Date

2018-07

Type of Work

journal articles preprints
Text

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

Rights

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

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.