: NLP Vocabulary: probabilistic graphical model

NLP methods and tools > graph theory > probabilistic graphical model

probabilistic graphical model

Another approach using probabilistic graphical model has been provided by (Ganea et al. 2016) with a factor graph that uses popularity-based prior. (Khalife & Vazirgiannis, 2019)
PGM consistently outperforms the Random-Subset baseline for different subsets with lower relative test error when compared against the full training and still yields significant speed up to reduce training time and maintain robustness. (Mittal, Sivasubramanian, Iyer, Jyothi & Ramakrishnan, 2022)
PGM is an adaptive subset selection algorithm that improves the training time of the ASR models while maintaining low relative test error as compared to the ASR model trained with the entire dataset. (Mittal, Sivasubramanian, Iyer, Jyothi & Ramakrishnan, 2022)
The authors then improved their work by using probabilistic graphical model so that the correlation between all the answers can be taken into consideration. (Magooda, Gomaa, Mahgoub, Ahmed, Rashwan, Raafat, Kamal & Al Sallab, 2016)
This probabilistic graphical model considers correlations between the neighborhood of words in a sentence and its features jointly with the corresponding labels. (Copara, Knafou, Naderi, Moro, Ruch & Teodoro, 2020)

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