It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms.
本课程涵盖了语法、语意及对话处理模型，著重在机器学习或是以语料库为基础的方法及演算法。
This thesis centers around the vocabulary learning in corpus-based contexts.
本论文围绕基于语料库的上下文词汇学习展开。
The Word Sense Disambiguation (WSD) study based on large scale real world corpus is performed using an unsupervised learning algorithm based on DGA improved Bayesian Model.
采用基于依存分析改进贝叶斯网络的无指导的机器学习方法对汉语大规模真实文本进行词义消歧实验。
应用推荐