CAJ | 학술논문

基于机器学习的分词模型可以借助科技词汇构词特征分析提升其在科技领域的适应性,本文对传统语言学的句法构词、韵律构词、语义构词几个方面理论进行总结归纳,融合术语学研究理论,围绕提升分词准确率的目的,提出了适用于科技词汇的构词特征标注系统,并对标注系统的结构进行了规划.这为科技词汇构词特征标注工作完成了前期的探索,为后期批量标注,辅助分词等环节提供了基础依据.
기우궤기학습적분사모형가이차조과기사회구사특정분석제승기재과기영역적괄응성,본문대전통어언학적구법구사、운률구사、어의구사궤개방면이론진행총결귀납,융합술어학연구이론,위요제승분사준학솔적목적,제출료괄용우과기사회적구사특정표주계통,병대표주계통적결구진행료규화.저위과기사회구사특정표주공작완성료전기적탐색,위후기비량표주,보조분사등배절제공료기출의거.
To improve the adaptability of word segmentation model in S&T domain, more features of S&T terms are needed. Based on the exploration on syntactic, rhetoric and semantic method of word formation, as well as terminology, tags are extracted and a labeling system is roughly designed aiming at improving the accuracy of word-parsing system. The research work on S&T word formation is not only the preliminary exploration of S&T terms tagging, but also the foundation of large size tagging and word segmentation.