A Multi-Task Approach to Open Domain Suggestion Mining (Student Abstract)

Authors

  • Minni Jain Delhi Technological University
  • Maitree Leekha Delhi Technological University
  • Mononito Goswami Delhi Technological University

DOI:

https://doi.org/10.1609/aaai.v34i10.7180

Abstract

Consumer reviews online may contain suggestions useful for improving the target products and services. Mining suggestions is challenging because the field lacks large labelled and balanced datasets. Furthermore, most prior studies have only focused on mining suggestions in a single domain. In this work, we introduce a novel up-sampling technique to address the problem of class imbalance, and propose a multi-task deep learning approach for mining suggestions from multiple domains. Experimental results on a publicly available dataset show that our up-sampling technique coupled with the multi-task framework outperforms state-of-the-art open domain suggestion mining models in terms of the F-1 measure and AUC.

Downloads

Published

2020-04-03

How to Cite

Jain, M., Leekha, M., & Goswami, M. (2020). A Multi-Task Approach to Open Domain Suggestion Mining (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13817-13818. https://doi.org/10.1609/aaai.v34i10.7180

Issue

Section

Student Abstract Track