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Language model adaptation using text data downloaded from the WWW is an efficient way to train a topic-specific LM. We are developing an unsupervised LM adaptation method using data in the Web. The one key point of unsupervised Web-based LM adaptation is how to select keywords to compose the search query. In this
. In this paper, we propose a web page-oriented and keywords-based approach to address this problem. Our approach includes two key components: keyword similarity measurement and keyword similarity based user segmentation. These two components serve as plugins and can be replaced with better algorithms or measurements
semantic net which can be applied to build personalized search engine and tested with single query keyword and multi ones by three different calculating policies. The test results show that it can affect the sort of pages. The personalized search based on vocabulary semantic net improves the quality of search results greatly.
The default page sorting algorithm in Nutch which is open source search engine is TF/IDF algorithm, but it's difficult to meet the demand of music page sorting. The paper presents a new page sorting algorithm bases on BM25 model for music users. According word count and keyword frequency in music web pages, the pages
The topic correlation judgment algorithm based on weight and threshold is proposed as for the problem that Web pages which are closely related to the given topic may be neglected due to not all keywords given by the users in the pages when users retrieve the topic they desire on the Internet. The algorithm retrieves
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.