A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
An HMM-Based Algorithm for Content Ranking and Coherence-Feature Extraction
2013
IEEE Transactions on Systems, Man & Cybernetics. Systems
In this paper, we propose an algorithm called coherence hidden Markov model (HMM) to extract coherence features and rank content. Coherence HMM is a variant of HMM and is used to model the stochastic process of essay writing and identify topics as hidden states, given sequenced clauses as observations. This study uses probabilistic latent semantic analysis for parameter estimation of coherence HMM. In coherence-feature extraction, support vector regression (SVR) with surface features and
doi:10.1109/tsmca.2012.2207104
fatcat:mp5txthn4zbmjf6ubxrqb5alzq