کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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516338 | 1449176 | 2009 | 6 صفحه PDF | دانلود رایگان |
MotivationTopic 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.MethodsWe 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.ResultsThe 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.
Journal: International Journal of Medical Informatics - Volume 78, Issue 12, December 2009, Pages e1–e6