Approaches to passage retrieval in full text information systems
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Subtopic structuring for full-length document access
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Document and passage retrieval based on hidden Markov models
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Improving automatic query expansion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Finding related pages in the World Wide Web
WWW '99 Proceedings of the eighth international conference on World Wide Web
Proceedings of the 10th international conference on World Wide Web
Toward a Qualitative Search Engine
IEEE Internet Computing
Context and Page Analysis for Improved Web Search
IEEE Internet Computing
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
A model of lexical attraction and repulsion
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Cohesion and collocation: using context vectors in text segmentation
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Statistical models for topic segmentation
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Extracting unstructured data from template generated web documents
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
WISDOM: Web Intrapage Informative Structure Mining Based on Document Object Model
IEEE Transactions on Knowledge and Data Engineering
Template detection for large scale search engines
Proceedings of the 2006 ACM symposium on Applied computing
A study about browsers in the Web and the Desktop
EATIS '07 Proceedings of the 2007 Euro American conference on Telematics and information systems
Page segmentation by web content clustering
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
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Internet search is one of the most important applications of the Web. A search engine takes the user's keywords to retrieve and to rank those pages that contain the keywords. One shortcoming of existing search techniques is that they do not give due consideration to the micro-structures of a Web page. A Web page is often populated with a number of small information units, which we call micro information units (MIU). Each unit focuses on a specific topic and occupies a specific area of the page. During the search, if all the keywords in the user query occur in a single MIU of a page, the top ranking results returned by a search engine are generally relevant and useful. However, if the query words scatter at different MIUs in a page, the pages returned can be quite irrelevant (which causes low precision). The reason for this is that although a page has information on individual MIUs, it may not have information on their intersections. In this paper, we propose a technique to solve this problem. At the off-line pre-processing stage, we segment each page to identify the MIUs in the page, and index the keywords of the page according to the MIUs in which they occur. In searching, our retrieval and ranking algorithm utilizes this additional information to return those most relevant pages. Experimental results show that this method is able to significantly improve the search precision.