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。
Moreover, based on the principles of Machine Learning, I modify the label of corpus to discriminate different situation of parsing structure.
另外还在对机器学习的思想理解的基础上，对语料进行标记上的修改以能区别句法分析中不同的情况。
This paper uses a corpus with break indices based on C-TOBI. Applying supervised learning method, some useful attempts are made in the field of automatic break indices intonation.
本文采用了一个基于CTOBI的停顿指数标注的语料库，利用有指导的学习方法对自动停顿指数标注方面做了一些有益的探索。
English corpus-based data-driven learning is a self-taught process, which includes self-monitoring, self-analysis and self-repair.
基于语料库的英语数据驱动学习是一个不断自我调控、自我分析、自我修正的自主性动态学习过程。
English corpus-based data-driven learning is a self-taught process, which includes self-monitoring, self-analysis and self-repair.
基于语料库的英语数据驱动学习是一个不断自我调控、自我分析、自我修正的自主性动态学习过程。
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