Open AccessResearch on Disambiguation Attributes Based on Semantic Hierarchical Model
Author(s) -
Linlin Yu,
Zhenxia Wang,
Li Su,
Dan Wei
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1861/1/012048
Subject(s) - computer science , artificial intelligence , semantic feature , natural language processing , feature (linguistics) , transfer of learning , set (abstract data type) , cluster analysis , semantic similarity , philosophy , linguistics , programming language
Feature extraction concepts and statistical classifiers began to be used in computer-aided detection and diagnosis, and many different feature learning techniques such as principal component analysis, image block clustering and dictionary methods are very popular. However, in the case of less labeled data, the application and effect of deep learning are restricted. The research content of this paper is based on items such as nodule multi-semantic and fine-grained classification and rating research of WGAN synthetic oversampling learning, multi-semantic assisted disambiguation research based on multi-level association transfer learning, and multi-label incremental learning research for small sample semantic hierarchical structure. Moreover, based on the synthesis of various semantic samples, multi-semantic association transfer, and multi-label incremental learning methods, a set of effective methods and application mechanisms for intelligent auxiliary semantic hierarchical structure disambiguation are formed by describing the problem definition, proposing research methods, and discussing the experimental effects of the proposed methods in detail.