Topic Segmentation with an Aspect Hidden Markov Model (2001)
| Citations: | 38 - 5 self |
BibTeX
@INPROCEEDINGS{Blei01topicsegmentation,
author = {David Blei and David M. Blei and Pedro Moreno and Pedro J. Moreno},
title = {Topic Segmentation with an Aspect Hidden Markov Model},
booktitle = {},
year = {2001},
pages = {343--348},
publisher = {ACM Press}
}
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Abstract
We present a novel probabilistic method for partially unsupervised topic segmentation on unstructured text. Previous approaches to this problem utilize the hidden Markov model framework (HMM). The HMM treats a document as mutually independent sets of words generated by a latent topic variable in a time series. We extend this idea by embedding the aspect model for text into the segmenting HMM. In doing so, we provide an intuitive topical dependency between words and a cohesive segmentation model. We apply this method to segment unbroken streams of New York Times articles as well as noisy transcripts of radio programs on SPEECHBOT 1 , an online audio archive indexed by an automatic speech recognition engine. We provide experimental comparisons between our technique and the HMM approach. Our results suggest that this technique can perform as well as the HMM method and in some cases even better.
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