A study of smoothing methods for language models applied to information retrieval
ACM Transactions on Information Systems (TOIS)
Language models for XML element retrieval
INEX'09 Proceedings of the Focused retrieval and evaluation, and 8th international conference on Initiative for the evaluation of XML retrieval
Estimating structural relevance of XML elements through language model
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Reading contexts for structured documents retrieval
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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In this paper we describe our participation in the INEX 2010 ad-hoc track. We participated in three retrieval tasks (restricted focused task, relevant-in-context, restricted relevant-in-context) and report our findings based on a single set of measure for all tasks. In this year's participation, we evaluate the performance of the standard language model that is more focused on a fixed number of relevant characters than on relevant paragraphs. Our findings are: 1) the simplest language model for document retrieval performs relatively well in the restricted focused task when using a fixed offset that is close to the average character distance from the beginning of a document to its main content; 2) a good result of document ranking does improve the performance of snippet retrieval; 3) stemming and stopword removal can further boost performance.