Abstract
Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or text classification. As learning is used increasingly in dynamic open environments where some new/test documents may not belong to any of the training classes, identifying these novel documents during classification presents an important problem. This problem is called open-world classification or open classification. This paper proposes a novel deep learning based approach. It outperforms existing state-of-the-art techniques dramatically.- Anthology ID:
- D17-1314
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2911–2916
- Language:
- URL:
- https://aclanthology.org/D17-1314
- DOI:
- 10.18653/v1/D17-1314
- Cite (ACL):
- Lei Shu, Hu Xu, and Bing Liu. 2017. DOC: Deep Open Classification of Text Documents. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2911–2916, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- DOC: Deep Open Classification of Text Documents (Shu et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/D17-1314.pdf