Paper: TEXT2TABLE: Medical Text Summarization System Based on Named Entity Recognition and Modality Identification

ACL ID W09-1324
Title TEXT2TABLE: Medical Text Summarization System Based on Named Entity Recognition and Modality Identification
Venue Workshop on Biomedical Natural Language Processing
Session
Year 2009
Authors

With the rapidly growing use of electronic health records, the possibility of large-scale clinical information extraction has drawn much attention. It is not, however, easy to ex- tract information because these reports are written in natural language. To address this problem, this paper presents a system that converts a medical text into a table structure. This system’s core technologies are (1) medi- cal event recognition modules and (2) a nega- tive event identification module that judges whether an event actually occurred or not. Regarding the latter module, this paper also proposes an SVM-based classifier using syn- tactic information. Experimental results dem- onstrate empirically that syntactic information can contribute to the method’s accuracy.