Using Graphs of Classifiers to Impose Declarative Constraints on Semi-Supervised Learning
Abstract
We propose a general approach to modeling semi-supervised learning (SSL) algorithms. Specifically, we present a declarative language for modeling both traditional supervised classification tasks and many SSL heuristics, including both well-known heuristics such as co-training and novel domain-specific heuristics. In addition to representing individual SSL heuristics, we show that multiple heuristics can be automatically combined using Bayesian optimization methods. We experiment with two classes of tasks, link-based text classification and relation extraction. We show modest improvements on well-studied link-based classification benchmarks, and state-of-the-art results on relation-extraction tasks for two realistic domains.
Cite
Text
Bing et al. "Using Graphs of Classifiers to Impose Declarative Constraints on Semi-Supervised Learning." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/201Markdown
[Bing et al. "Using Graphs of Classifiers to Impose Declarative Constraints on Semi-Supervised Learning." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/bing2017ijcai-using/) doi:10.24963/IJCAI.2017/201BibTeX
@inproceedings{bing2017ijcai-using,
title = {{Using Graphs of Classifiers to Impose Declarative Constraints on Semi-Supervised Learning}},
author = {Bing, Lidong and Cohen, William W. and Dhingra, Bhuwan},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {1454-1460},
doi = {10.24963/IJCAI.2017/201},
url = {https://mlanthology.org/ijcai/2017/bing2017ijcai-using/}
}