Identifying Segment Topics in Medical Dictations

Abstract

In this paper, we describe the use of lexical and semantic features for topic classification in dictated medical reports. First, we employ SVM classification to assign whole reports to coarse work-type categories. Afterwards, text segments and their topic are identified in the output of automatic speech recognition. This is done by assigning work-type-specific topic labels to each word based on features extracted from a sliding context window, again using SVM classification utilizing semantic features. Classifier stacking is then used for a posteriori error correction, yielding a further improvement in classification accuracy.