人类通常是通过自然语言进行沟通的。
The way humans normally communicate is through natural language.
沃森表示了计算机能力的重大突破，理解人类用于捕捉和传播知识的自然语言。
Watson represents a major breakthrough in the ability of computers to understand natural language, which humans use to capture and communicate knowledge.
C+ +仍然是一个典型的“高级”计算机语言(即，保持了人类自然语言的特点)，而且还有数以百万计的程序员在使用。
C + + remains the archetypal "high level" computer language (that is, one that preserves the features of natural, human language), and it is still used by millions of programmers.
人类的语言一般称为自然语言。
语义选择限制是人类知识的重要组成部分，被广泛用于多种自然语言处理任务。
Selectional preference is an important part of human knowledge, and has been widely used in many natural language processing tasks.
目前的自然语言处理系统大多是基于人类语言的句法、语义理论之上的。
Currently most natural language processing systems are based on syntactic and semantic theories of human language.
本课程是研究所的自然语言处理入门课程，主要课程重点是从电脑的观点研究人类语言。
This course is a graduate level introduction to natural language processing, the primary concern of which is the study of human language from a computational perspective.
隐喻是自然语言中普遍存在的现象，也是人类的生存方式，它根植于语言思维和文化中。
It's also a way of human life being rooted in language thinking and culture.
人类社会正在从工业社会迈向信息社会，信息的主要载体是自然语言，即人类彼此交流所使用的语言。
Human society is moving from the industrial society into an information society, and information is the main carrier of natural language, which is used for communicating by human being.
作为自然语言理解的一项研究重点，语义分析旨在将人类的自然语言转化为计算机能够理解的形式化语言。
As a research focus in natural language understanding area, the purpose of semantic analysis is to transfer the mankind's natural language into formal language that computer can understand.
模糊性是自然语言以及人类思维的内在特性。
It is an intrinsic property of natural language as well as human thinking.
通过语义分析可以理解自然语言语句，并进行深入的知识获取和推理，使计算机能够与人类无障碍的沟通。
By semantic parsing, the natural language sentence can be understood and knowledge acquirement and inference become possible. Consequently computer and human being can communicate freely.
目前基于文本的情感计算虽然取得了很大的进展，但在人类情感和自然语言的模糊本质属性方面研究不足。
Although the current text-based affective computing has made great progress, there are also deficiencies on the study of fuzzy essential attributes of natural language and human sentiment.
目前基于文本的情感计算虽然取得了很大的进展，但在人类情感和自然语言的模糊本质属性方面研究不足。
Although the current text-based affective computing has made great progress, there are also deficiencies on the study of fuzzy essential attributes of natural language and human sentiment.
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