提出了一种面向网络答疑系统的无词典分词方法。
A segmentation algorithm without dictionary based on network-oriented natural language question answering system is proposed.
最后，给出方法类答案获取的实现及其在自然语言智能网络答疑系统中的应用。
Finally, this paper gives the implementation of acquiring answers to method type questions as well as the application in natural language intelligent network question Answering system.
目前，该方法已经用于基于自然语言的智能网络答疑系统中，并取得了较好的效果。
The innovative solution has been already applied to the natural language intelligent network Question Answering system. The application achieved well results.
因此，结合网络优势，研究和开发富有特色的网络答疑系统就成为了解决问题的有效途径。
Thus, taking the advantage of network Resigning and developing characteristic network-based answering system become the effective way of solve the problem.
课题重点研究了网络考试系统中智能组卷的方法和实时、非实时网络答疑系统的实现途径。
The item study the methods of composing test paper of exam on web and the way to realize real-time or unreal-time answer system.
本系统模型实现了答疑系统的语义理解，提高了现有网络答疑系统的效率，并具有一定的智能性。
The system model realizes semantic understanding of Question Answer system improving efficiency of the present Question Answer system and with certain intelligence.
利用当前比较先进的自然语言分析技术、全文检索技术、数据挖掘技术等，设计了比较全面的网络课程的智能答疑系统。
Using the present advanced technologies such as nature language, full-text searching and data mining, this paper designed a full-round intelligent question-answer system of network course.
其次，在文中还以较大的篇幅分析了远程网络教学中辅导答疑系统的现状和存在的技术问题。
Second, the passage analyzed long-range network teaching help reply system about current circumstance and exhausted technical problem through long space.
系统主要由四个子系统组成：信息管理子系统、网络授课子系统、网络答疑子系统及网络考试子系统。
This system mainly consists of four pieces of module: information management subsystem, network teaching subsystem, network answering questions subsystem and network examination subsystem.
针对以上缺点，本文给出一个应用语义网络原理构筑起来的智能答疑系统。
To solve this problem, this paper presents an agent-based intelligent tutoring system by applying the semantic-net principle.
针对以上缺点，本文给出一个应用语义网络原理构筑起来的智能答疑系统。
To solve this problem, this paper presents an agent-based intelligent tutoring system by applying the semantic-net principle.
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