A Method to Position the Natural Language Topic Change Accurately Based on Neural Network and Hierarchies of Word Change
一种新颖的自然语言主题转换精确定位方法

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.