面向特定领域的理解型中文自动文摘系统

AN UNDERSTANDING BASED CHINESE AUTOMATIC ABSTRACT SYSTEM IN SPECIAL FIELD

自动文摘是计算机通信网提供智能业务的关键技术之一 .介绍了一个面向特定领域——神经网络学习算法的理解型中文自动文摘系统 .与其他文摘系统相比 ,该系统首次采用了基于全信息模型的自然语言理解理论 ,通过设计义块组配的方法避开了传统的语法语义分析系统所遇到的各种难题 ,并通过语用信息制导 ,将语句理解与文摘信息提取直接相连 ,大大提高了理解的效率和文摘生成的速度 .测试结果表明该系统的整套方法是行之有效的 .

is one of the key technologies for computer communication networks to provide intelligent online services for users. An understanding based Chinese automatic abstract system dedicated to a special field of neural networks learning algorithm (NNLA) is introduced in this paper, which is called LADIES (Literature Abstract and Digest Information Extract System). Compared with other automatic abstract systems, LADIES uses the comprehensive information model based natural language processing theory for the first time. Comprehensive information is a name for the entirety of grammatical, semantic and pragmatic information of words. As a result, this model can describe the knowledge of words completely, which is very useful for sentence understanding. Semantic chunk assembling algorithm is another fine characteristic of LADIES, which is designed for grammatical and semantic sentence analyzing so as to solve the inherent difficulties of traditional sentence analyzing systems. Traditional systems need hard efforts to recognize the complicated grammatical structure of the whole sentence without understanding its semantic meaning, using word as the basic analyzing element. LADIES has also directly associated the sentence understanding with abstract information extraction with pragmatic information as the driver. Results of many experiments show that the whole set of method is very effective.