Combining hidden Markov models and latent semantic analysis for topic segmentation and labeling: Method and clinical application
International journal of medical informatics, 2009•Elsevier
MOTIVATION: Topic segmentation and labeling systems enable fine-grained information
search. However, previously proposed methods require annotated data to adapt to different
information needs and have limited applicability to texts with short segment length.
METHODS: We introduce an unsupervised method based on a combination of hidden
Markov models and latent semantic analysis which allows the topics of interest to be defined
freely, without the need for data annotation, and can identify short segments. RESULTS: The …
search. However, previously proposed methods require annotated data to adapt to different
information needs and have limited applicability to texts with short segment length.
METHODS: We introduce an unsupervised method based on a combination of hidden
Markov models and latent semantic analysis which allows the topics of interest to be defined
freely, without the need for data annotation, and can identify short segments. RESULTS: The …
MOTIVATION
Topic segmentation and labeling systems enable fine-grained information search. However, previously proposed methods require annotated data to adapt to different information needs and have limited applicability to texts with short segment length.
METHODS
We introduce an unsupervised method based on a combination of hidden Markov models and latent semantic analysis which allows the topics of interest to be defined freely, without the need for data annotation, and can identify short segments.
RESULTS
The method is evaluated on intensive care nursing narratives and motivated by information needs in this domain. The method is shown to considerably outperform a keyword-based heuristic baseline and to achieve a level of performance comparable to that of a related supervised method trained on 3600 manually annotated words.
Elsevier
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