Abstract
This paper describes our submission to IJCNLP 2017 shared task 4, for predicting the tags of unseen customer feedback sentences, such as comments, complaints, bugs, requests, and meaningless and undetermined statements. With the use of a neural network, a large number of deep learning methods have been developed, which perform very well on text classification. Our ensemble classification model is based on a bi-directional gated recurrent unit and an attention mechanism which shows a 3.8% improvement in classification accuracy. To enhance the model performance, we also compared it with several word-embedding models. The comparative results show that a combination of both word2vec and GloVe achieves the best performance.- Anthology ID:
- I17-4029
- Volume:
- Proceedings of the IJCNLP 2017, Shared Tasks
- Month:
- December
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 174–179
- Language:
- URL:
- https://aclanthology.org/I17-4029
- DOI:
- Cite (ACL):
- Nan Wang, Jin Wang, and Xuejie Zhang. 2017. YNU-HPCC at IJCNLP-2017 Task 4: Attention-based Bi-directional GRU Model for Customer Feedback Analysis Task of English. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 174–179, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- YNU-HPCC at IJCNLP-2017 Task 4: Attention-based Bi-directional GRU Model for Customer Feedback Analysis Task of English (Wang et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/I17-4029.pdf