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Abstract

A substantial fraction of web search queries contain references to entities, such as persons, organizations, and locations. Recently, methods that exploit named entities have been shown to be more effective for query expansion than traditional pseudorelevance feedback methods. In this article, we introduce a supervised learning approach that exploits named entities for query expansion using Wikipedia as a repository of high-quality feedback documents. In contrast with existing entity-oriented pseudorelevance feedback approaches, we tackle query expansion as a learning-to-rank problem. As a result, not only do we select effective expansion terms but we also weigh these terms according to their predicted effectiveness. To this end, we exploit the rich structure of Wikipedia articles to devise discriminative term features, including each candidate term's proximity to the original query terms, as well as its frequency across multiple article fields and in category and infobox descriptors. Experiments on three Text REtrieval Conference web test collections attest the effectiveness of our approach, with gains of up to 23.32% in terms of mean average precision, 19.49% in terms of precision at 10, and 7.86% in terms of normalized discounted cumulative gain compared with a state-of-the-art approach for entity-oriented query expansion.

Date

22. 8.2014 17:07:50

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Abstract

In this work, we investigate the problem of using the block structure of Web pages to improve ranking results. Starting with basic intuitions provided by the concepts of term frequency (TF) and inverse document frequency (IDF), we propose nine block-weight functions to distinguish the impact of term occurrences inside page blocks, instead of inside whole pages. These are then used to compute a modified BM25 ranking function. Using four distinct Web collections, we ran extensive experiments to compare our block-weight ranking formulas with two other baselines: (a) a BM25 ranking applied to full pages, and (b) a BM25 ranking that takes into account best blocks. Our methods suggest that our block-weighting ranking method is superior to all baselines across all collections we used and that average gain in precision figures from 5 to 20% are generated.

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Abstract

In this article, we present a study about classification methods for large-scale categorization of product offers on e-shopping web sites. We present a study about the performance of previously proposed approaches and deployed a probabilistic approach to model the classification problem. We also studied an alternative way of modeling information about the description of product offers and investigated the usage of price and store of product offers as features adopted in the classification process. Our experiments used two collections of over a million product offers previously categorized by human editors and taxonomies of hundreds of categories from a real e-shopping web site. In these experiments, our method achieved an improvement of up to 9% in the quality of the categorization in comparison with the best baseline we have found.

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Abstract

In this article we present FLUX-CiM, a novel method for extracting components (e.g., author names, article titles, venues, page numbers) from bibliographic citations. Our method does not rely on patterns encoding specific delimiters used in a particular citation style. This feature yields a high degree of automation and flexibility, and allows FLUX-CiM to extract from citations in any given format. Differently from previous methods that are based on models learned from user-driven training, our method relies on a knowledge base automatically constructed from an existing set of sample metadata records from a given field (e.g., computer science, health sciences, social sciences, etc.). These records are usually available on the Web or other public data repositories. To demonstrate the effectiveness and applicability of our proposed method, we present a series of experiments in which we apply it to extract bibliographic data from citations in articles of different fields. Results of these experiments exhibit precision and recall levels above 94% for all fields, and perfect extraction for the large majority of citations tested. In addition, in a comparison against a state-of-the-art information-extraction method, ours produced superior results without the training phase required by that method. Finally, we present a strategy for using bibliographic data resulting from the extraction process with FLUX-CiM to automatically update and expand the knowledge base of a given domain. We show that this strategy can be used to achieve good extraction results even if only a very small initial sample of bibliographic records is available for building the knowledge base.

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Date

24. 6.2012 14:29:10