Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches
Akshay Soni, Aasish Pappu, Jerry Chia-mau Ni, Troy Chevalier
Abstract
In Multilabel Learning (MLL) each training instance is associated with a set of labels and the task is to learn a function that maps an unseen instance to its corresponding label set. In this paper, we present a suite of – MLL algorithm independent – post-processing techniques that utilize the conditional and directional label-dependences in order to make the predictions from any MLL approach more coherent and precise. We solve constraint optimization problem over the output produced by any MLL approach and the result is a refined version of the input predicted label set. Using proposed techniques, we show absolute improvement of 3% on English News and 10% on Chinese E-commerce datasets for P@K metric.- Anthology ID:
- I17-2011
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 61–66
- Language:
- URL:
- https://aclanthology.org/I17-2011
- DOI:
- Cite (ACL):
- Akshay Soni, Aasish Pappu, Jerry Chia-mau Ni, and Troy Chevalier. 2017. Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 61–66, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches (Soni et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/I17-2011.pdf