A Field Relevance Model for Structured Document Retrieval
Many search applications involve documents with structure or fields. Since query terms often are related to specific structural components, mapping queries to fields and assigning weights to those fields is critical for retrieval effectiveness. Although several field-based retrieval models have been developed, there has not been a formal justification of field weighting.
In this work, we aim to improve the field weighting for structured document retrieval. We first introduce the notion of field relevance as the generalization of field weights, and discuss how it can be estimated using relevant documents, which effectively implements relevance feedback for field weighting. We then propose a framework for estimating field relevance based on the combination of several sources. Evaluation on several structured document collections show that field weighting based on the suggested framework improves retrieval effectiveness significantly.
KeywordsRelevant Document Relevance Feedback Retrieval Model Query Term Mean Average Precision
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- 2.Craswell, N., Hugo Zaragoza, S.R.: Microsoft cambridge at trec-14: Enterprise track. In: The Fourteenth Text REtrieval Conference (2005)Google Scholar
- 3.Craswell, N., de Vries, A.P.: Overview of the trec-2005 enterprise track. In: The Fourteenth Text REtrieval Conf. Proc. (2005)Google Scholar
- 4.Kim, J., Croft, W.B.: Retreival experiments using pseudo-desktop collections. In: Proceedings of CIKM 2009, Hong Kong, China, pp. 1297–1306 (2009)Google Scholar
- 6.Lavrenko, V.: A generative theory of relevance. PhD thesis, AAI3152722 (2004)Google Scholar
- 8.Lavrenko, V., Yi, X., Allan, J.: Information retrieval on empty fields. In: HLT-NAACL, pp. 89–96 (2007)Google Scholar
- 9.Li, X., Wang, Y.-Y., Acero, A.: Extracting structured information from user queries with semi-supervised conditional random fields. In: SIGIR 2009. ACM, New York (2009)Google Scholar
- 13.Ponte, J., Croft, W.B.: A language modeling approach to information retrieval, pp. 275–281. ACM, New York (1998)Google Scholar
- 16.Yi, X., Allan, J., Croft, W.B.: Matching resumes and jobs based on relevance models. In: SIGIR, pp. 809–810 (2007)Google Scholar