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Understanding Hierarchical Processes
(2022-11-22)
Hierarchical stochastic processes, such as the hierarchical Dirichlet process, hold an important position as a modelling tool in statistical machine learning, and are even used in deep neural networks. They allow, for ...
A systematic review of the use of topic models for short text social media analysis
(2023-03-14)
Recently, research on short text topic models has addressed the challenges of social media datasets. These models are typically evaluated using automated measures. However, recent work suggests that these evaluation measures ...
AUC Maximization for Low-Resource Named Entity Recognition
(2023-04-13)
Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER model. Both of these traditional objective ...