A MEDICAL TEXT CLASSIFICATION APPROACH WITH ZEN AND CAPSULE NETWORK

Abstract

TEXT CLASSIFICATION IS AN IMPORTANT TOPIC IN NATURAL LANGUAGE PROCESSING, WITH THE DEVELOPMENT OF SOCIAL NETWORK, MANY QUESTION-AND-ANSWER PAIRS REGARDING HEALTH-CARE AND MEDICINE FLOOD SOCIAL PLATFORMS. IT IS OF GREAT SOCIAL VALUE TO MINE AND CLASSIFY MEDICAL TEXT AND PROVIDE TARGETED MEDICAL SERVICES FOR PATIENTS. THE EXISTING ALGORITHMS OF TEXT CLASSIFICATION CAN DEAL WITH SIMPLE SEMANTIC TEXT, ESPECIALLY IN THE FIELD OF CHINESE MEDICAL TEXT, THE TEXT STRUCTURE IS COMPLEX AND INCLUDES A LARGE NUMBER OF MEDICAL NOMENCLATURE AND PROFESSIONAL TERMS, WHICH ARE DIFFICULT FOR PATIENTS TO UNDERSTAND. WE PROPOSE A CHINESE MEDICAL TEXT CLASSIFICATION MODEL USING A BERT-BASED CHINESE TEXT ENCODER BY N-GRAM REPRESENTATIONS (ZEN) AND CAPSULE NETWORK, WHICH REPRESENT FEATURE USES THE ZEN MODEL AND EXTRACT THE FEATURES BY CAPSULE NETWORK, WE ALSO DESIGN A N-GRAM MEDICAL DICTIONARY TO ENHANCE MEDICAL TEXT REPRESENTATION AND FEATURE EXTRACTION. THE EXPERIMENTAL RESULTS SHOW THAT THE PRECISION, RECALL AND F1-SCORE OF OUR MODEL ARE IMPROVED BY 10.25%, 11.13% AND 12.29%, RESPECTIVELY, COMPARED WITH THE BASELINE MODELS IN AVERAGE, WHICH PROVES THAT OUR MODEL HAS BETTER PERFORMANCE.