Overview of the 2021 Key Point Analysis Shared Task
Roni Friedman-Melamed, Lena Dankin, et al.
EMNLP 2021
We propose a novel feature selection method based on a variable memory Markov (VMM) model. The VMM was originally proposed as a generative model trying to preserve the original source statistics from training data. We extend this technique to simultaneously handle several sources, and further apply a new criterion to prune out nondiscriminative features out of the model. This results in a multiclass discriminative VMM (DVMM), which is highly efficient, scaling linearly with data size. Moreover, we suggest a natural scheme to sort the remaining features based on their discriminative power with respect to the sources at hand. We demonstrate the utility of our method for text and protein classification tasks.
Roni Friedman-Melamed, Lena Dankin, et al.
EMNLP 2021
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HLDVT 2005
Noam Slonim, Ehud Aharoni, et al.
IJCAI 2013
Orith Toledo-Ronen, Matan Orbach, et al.
EMNLP 2020