Text Subject Cut Points Technology And Rocchio Models In Information Retrieval Applications

In current century, how to achieve useful information for the users from huge mount of information is one of the main problems confronted with people. With the development of research and application in Information Retrieval(IR), the IR technology is divided into Question/Answer, Web, Interactive and Text Filter and so on. To advance i:he precision of IR system and make users more satisfied with the results, researchers have merged relevant technologies and theories based on current Natural Language Process(NLP) and IR to implement the goal.The backgrovind of this paper is Text Retrieval Conference (TREC), High Accuracy Retrieval of Document (HARD). In this paper, the characteristics of traditional vector model and probabilistic model are introduced . Although the modern IR is not restricted in full text retrieval, these two models are widely and effectively used in the first step in kinds of modern IR. Then the threads in segmenting document into different topic is introduced, which includes statistical methods and semantic network. Then, the Rocchio model characteristics in text filter are analyzed. Then, shallow technologies of NLP used in this paper are introduced. At last, to make the user query more precise, some elements are introduced.To fulfill the requirement and characteristics of this track, which include paragraph-based and a relevant document supplied by user before retrieval, the rocchio model and vector model are merged to compute relevance between query and document. Then, Gradient Decrease method is used to train the parameters of rocchio model. Then, based on the paragraph-level relevance, the sorted documents are returned.Based on such technologies, experiments are done and results are analyzed.