In this paper, the author presents a novel information extraction system that analyses fire service reports. Although the reports contain valuable information concerning fire and rescue incidents, the narrative information in these reports has received little attention as a source of data. This is because of the challenges associated with processing these data and making sense of the contents through the use of machines. Therefore, a new issue has emerged: How can we bring to light valuable information from the narrative portions of reports that currently escape the attention of analysts? The idea of information extraction and the relevant system for analysing data that lies outside existing hierarchical coding schemes can be challenging for researchers and practitioners. Furthermore, comprehensive discussion and propositions of such systems in rescue service areas are insufficient. Therefore, the author comprehensively and systematically describes the ways in which information extraction systems transform unstructured text data from fire reports into structured forms. Each step of the process has been verified and evaluated on real cases, including data collected from the Polish Fire Service. The realisation of the system has illustrated that we must analyse not only text data from the reports but also consider the data acquisition process. Consequently, we can create suitable analytical requirements. Moreover, the quantitative analysis and experimental results verify that we can (1) obtain good results of the text segmentation (F-measure 95.5%) and classification processes (F-measure 90%) and (2) implement the information extraction process and perform useful analysis.
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