关键词: 文本分割, 词语量化关系, 句子相关性度量, 间隔点, 分隔值

Abstract: With the quick expanding of the Internet information resource，the task of processing a mass of non-structured texts is faced with a huge challenge.Text segmentation based on the topic is a very important preprocessing step of text processing，and the performance of text segmentation technique has an immediate influence on the result of these tasks，such as Information Retrieval，Text Summarization and Q-A system.However，there exists two key problems in the text segmentation task，namely，how to measure the relevance of between topics and how to make a strategy for identifying the segment boundary based on the relevance of the context.In order to solve the above problems，this paper presents a new approach to measure the relevance of between sentences based on the Quantified Conceptual Relations（QCR） extracted from Modern Chinese Standard Dictionary（MCSD），and built a model to calculate the Segmentation Value of the gap point of between sentences for the task of text segmentation oriented sentence-level（no paragraph-level）.The experiment results show that this approach has achieved a lower average error rate pk than that of state-of-the-art methods in the task of Chinese Text Segmentation.

Key words: text segmentation, quantified conceptual relations, inter-sentence relevance measure, gap point, segmentation value