一种新颖的自然语言主题转换精确定位方法

自然语言的主题转换是自然语言理解的一个重要线索.语言处理通常是针对不同的主题有不同的数据库和处理方法.因此，如何找到文本中的主题转换点是语言处理中的一个重要内容.该技术在语言理解、文本自动索引以及语言模型的建立等方面都有重要意义.该文以文本主题转换时的词汇突变为表征，提出和定义了反映词汇突变的4个参数，将这4个参数作为输入，利用BP网作为判决工具，建立了一个在不同尺度下文本词汇变化的层次结构模型，实现了一种精确的文本主题转换点的定位方法，其定位精度在一个句子左右.

A Method to Position the Natural Language Topic Change Accurately Based on Neural Network and Hierarchies of Word Change

The topic change of natural language is a very important clue of natural language understanding. Since different database and method should be used when different topic text is processed generally, it is important to find the topic change point in text. This technology is very useful in natural language understanding, text indexing and language model building, etc. In this paper, using the burst character of vocabulary in the change of topic, the authors present four parameters to reflect this character. They propose a method of text segmenting based on BP algorithm and hierarchical structure of word change. The accuracy of this method is about one sentence.