Word Sense Disambiguation with Distribution Estimation
By Yee Seng Chan, Hwee Tou Ng. 2005
83 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Bernhard Schölkopf
- Dacheng Tao
- Mingming Gong
- Kun Zhang
- Tongliang Liu
- Yoshua Bengio
- Krikamol Muandet
- Mingsheng Long
Citations in the corpus
- Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift (2018, background): “Owing to the impossibility of correcting for shift absent assumptions [3], these papers often assume either covariate shift q(x, y) = q(x)p(y|x) [15, 45, 49] or label shift q(x, y) = q(y)p(x|y) [7, 29, 38, 48, 56].”
- Detecting and Correcting for Label Shift with Black Box Predictors (2018, methodology): “Compared to approaches based on Kernel Mean Matching (KMM) (Zhang et al., 2013), EM (Chan & Ng, 2005), and Bayesian inference (Storkey, 2009”
- Domain Adaptation under Target and Conditional Shift (2013, methodology): “For classification problems, it is possible to estimate P te Y in an iterative way by maximizing the likelihood on x te , for instance, with the EM algorithm (Chan & Ng, 2005); however, such approaches involve estimation of P tr X|Y explicitly, which is difficult for high-dimensional problems .”
- Syntactic/Semantic Structures for Textual Entailment Recognition (2010, methodology): “WordNet similarities have been heavily used in previous NLP work (Chan and Ng, 2005; Agirre et al., 2009).”
- Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives (2022, background): “Moreover, the label shift (Chan and Ng, 2005), a.k.a. target shift, indicates the sample proportion of involved classes is different between two domains.”
- Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation (2022, background): “Some approaches [6, 19] try to address this problem, but they typically assume that the conditional distribution is the same across domains, which is not realistic in practice.”
- LTF: A Label Transformation Framework for Correcting Label Shift (2020, background): “Chan & Ng (2005) proposed an expectation-LTF maximization algorithm that requires estimation of the conditional distribution P X | Y .”