HCRL at NTCIR-11 MedNLP-2 Task
نویسندگان
چکیده
This year’s MedNLP-2 [1] has two tasks: Extraction task (Task 1) and Normalization task (Task 2). We tested both machine learning based methods and an ad-hoc rule-based method for the two tasks. For the Extraction Task, a two-stage approach (first, the machine learning based method is applied to identify c tags, and second, the rule-based method is applied to modality features) obtained higher results. For the Normalization Task, the machine learning based method obtained higher results for training data, but the simple pattern-matching method obtained higher results for test data.
منابع مشابه
HCRL at NTCIR-10 MedNLP Task
This year's MedNLP[1] has two tasks: de-identification and complaint and diagnosis. We tested both machine learning based methods and an ad-hoc rule-based method for the two tasks. For the de-identification task, the rule-based method got slightly higher results, while for the complaint and diagnosis task, the machine learning based method had much higher recalls and overall scores. These resul...
متن کاملOverview of the NTCIR-11 MedNLP-2 Task
Electronic medical records are now often replacing paper documents, and thus the importance of information processing in medical fields has increased. We have already organized the NTCIR-10 MedNLP pilot task. It has been the very first shared task attempt to evaluate technologies to retrieve important information from medical reports written in Japanese, whereas the NTCIR-11 MedNLP-2 task has b...
متن کاملNCU IISR System for NTCIR-11 MedNLP-2 Task
This paper describes NCU IISR’s Japanese ICD-10 Code Linking system for NTCIR-11 MedNLP. Our system uses Conditional Random Fields (CRFs) to label ICD-10 mentions and temporal expressions. We also use CRFs to detect the modalities of the ICD-10 mentions. To resolve the problem of ICD-10 mention normalization, we use the Lucene engine to link mentions to the corresponding ICD-10 database entries...
متن کاملTechnical Report of Uni2014 in NTCIR-11 MedNLP-2
This paper describes approach and evaluation using CRFs and dictionary matching in Task1 (Extraction of complaint and diagnosis Task) and dictionary matching in Task2 (Normalization of complaint and diagnosis Task). Team name Uni2014 Subtasks ・Task1 (Extraction Task) ・Task2 (Normalization Task)
متن کاملkyoto: 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 s...
متن کامل