Topic segmentation with an aspect hidden Markov model

By David M. Blei, Pedro J. Moreno Mengibar. 2001

204 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Learning World Graphs to Accelerate Hierarchical Reinforcement Learning (2019, neutral): “ducts from learning world graph, hence using pivotal states with its coupled G wis more favorable over V rand. 6 Related Works Pivotal state discovery is related to unsupervised sequence segmentation [17, 47, 76, 13, 16] and option or sub-task discovery in the context of RL [48, 6, 72, 30, 56, 53, 5”
Survey on Aspect-Level Sentiment Analysis (2016, background): “transitions [57], the authors deem those computationally too expensive.”
Hidden Topic Markov Models (2007, methodology): “A somewhat related model is the aspect HMM model [2], though it models unstructured data that contains stream of words.”
CompILE: Compositional Imitation Learning and Execution (2018, background): “Unsupervised segmentation and encoding of sequential data has also received considerable attention in natural language and speech processing (Blei & Moreno, 2001; Goldwater et al., 2009; Chan et al., 2017; Wang et al., 2017; Tang et al., 2018), and in the analysis of sequential activity data…”
Learning Influence among Interacting Markov Chains (2005, methodology): “We embedded PLSA into our model by treating the states of individual players as instances of PLSA topics (similar to [5]).”
Modeling online reviews with multi-grain topic models (2008, methodology): “This problem can be addressed by explicitly modeling topic transitions [5, 15, 33, 32, 28, 16], but these topic n-gram models are considerably more computationally expensive.”