Combining document representations for known-item search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Length normalization in XML retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical language models for XML component retrieval
INEX'04 Proceedings of the Third international conference on Initiative for the Evaluation of XML Retrieval
XML search: languages, INEX and scoring
ACM SIGMOD Record
Overview of the INEX 2007 Ad Hoc Track
Focused Access to XML Documents
Using Language Models and Topic Models for XML Retrieval
Focused Access to XML Documents
Flexible document-query matching based on a probabilistic content and structure score combination
Proceedings of the 2010 ACM Symposium on Applied Computing
Exploiting semantic tags in XML retrieval
INEX'09 Proceedings of the Focused retrieval and evaluation, and 8th international conference on Initiative for the evaluation of XML retrieval
Fast and incremental indexing in effective and efficient XML element retrieval systems
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Estimating structural relevance of XML elements through language model
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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This paper explores the possibility of using a modified Expectation-Maximization algorithm to estimate parameters for a simple hierarchical generative model for XML retrieval. The generative model for an XML element is estimated by linearly interpolating statistical language models estimated from the text of the element, the parent element, the document element, and its children elements. We heuristically modify EM to allow the incorporation of negative examples, then attempt to maximize the likelihood of the relevant components while minimizing the likelihood of non-relevant components found in training data. The technique for incorporation of negative examples provide an effective algorithm to estimate the parameters in the linear combination mentioned. Some experiments are presented on the CO.Thorough task that support these claims.