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    <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.</abstract>
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%0 Conference Proceedings
%T Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches
%A Soni, Akshay
%A Pappu, Aasish
%A Ni, Jerry Chia-mau
%A Chevalier, Troy
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F soni-etal-2017-post
%X 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.
%U https://aclanthology.org/I17-2011
%P 61-66

Markdown (Informal)

[Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches](https://aclanthology.org/I17-2011) (Soni et al., IJCNLP 2017)

• Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches (Soni et al., IJCNLP 2017)

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.