kyoto: Kyoto University Baseline at the NTCIR-11 MedNLP-2 Task

Since more electronic records are now used at medical scenes, the importance of technical development for analyzing such electronically provided information has been increasing significantly. This NTCIR-11 MedNLP-2 Task is designed to meet this situation. This task is a shared task that evaluates natural language processing technologies especially on Japanese medical texts. The task has three subtasks: (1) the Extraction task, which is to recognize complaints and diagnoses in medical texts; (2) the Normalization task, which is the ICD-coding task for complaint and diagnosis in the texts; (3) free task. This paper is the report on our results. For the Extraction task, we used a standard named entity recognition technique that is based on conditional random fields. For the normalization task, we used the string similarity between the input term and the MEDIS ICD-10 dictionary. For the free task, we proposed to design a glossary of medical terms for patients. The experimental results in the Extraction task showed reasonably high performance (precision: 77.10%, recall: 17.74%, F-measure: 58.97). However, the results in the Normalization task showed low performance (precision: 33.69%, recall: 33.69%, Fmeasure: 33.69). Finally, we show an example of the glossary described above as the result of the free task.