Active Learning with Sampling by Uncertainty and Density for Word Sense Disambiguation and Text Classification
By Jingbo Zhu, Huizhen Wang, T. Yao, Benjamin Ka-Yin T'sou. 2008
219 citations recorded by the source corpus.
This paper addresses two issues of active learning. Firstly, to solve a problem of uncertainty sampling that it often fails by selecting outliers, this paper presents a new selective sampling technique, sampling by uncertainty and density (SUD), in which a k-Nearest-Neighbor-based density measure is adopted to determine whether an unlabeled example is an outlier. Secondly, a technique of sampling by clustering (SBC) is applied to build a representative initial training data set for active learning. Finally, we implement a new algorithm of active learning with SUD and SBC techniques. The experimental results from three real-world data sets show that our method outperforms competing methods, particularly at the early stages of active learning.
Subfields: Artificial Intelligence, Epidemiology, Natural Language Processing, Information Extraction, Statistical Learning Theory
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Douwe Kiela
- Mohit Bansal
- Nazneen Rajani
- Tristan Thrush
- Yacine Jernite
- Yejin Choi
- Margaret Mitchell
- Angelina McMillan-Major
In Natural Language Processing
- Tristan Thrush
- Douwe Kiela
- Nazneen Rajani
- Angelina McMillan-Major
- Yacine Jernite
- Alexandra Sasha Luccioni
- Margaret Mitchell
- Jason Aaron Edward Weston
In Information Extraction
In Statistical Learning Theory
Citations in the corpus
- Measuring Data (2022, background): “Relevant measures for density in datasets include: General Data Measures KNN Density (Loftsgaarden and Quesenberry, 1965; Zhu et al., 2008): Average similarity between examples, which can be used for determining whether an unlabeled example is an outlier compared to other examples and finding…”