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. We introduce an unsupervised method based on a combination of Hidden Markov Models and latent semantic indexing which allows the topics of interest to be defined freely, without the need for data annotation, and can identify short segments. The method is evaluated in an application domain of intensive care nursing narratives. It 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.
Ginter proposed an unsupervised method, based on hidden Markov models, which was combined with latent semantic analysis to define topics of interest without necessarily data annotation; this method could also be used to identify short segments REF ).
Combining Hidden Markov Models and Latent Semantic Analysis for Topic Segmentation and Labeling: Method and Clinical Application
International journal of medical informatics
International journal of medical informatics
Computer Science Medicine