Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Tag-based social interest discovery
Proceedings of the 17th international conference on World Wide Web
Query dependent pseudo-relevance feedback based on wikipedia
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Social annotation in query expansion: a machine learning approach
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Associative tag recommendation exploiting multiple textual features
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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We here propose a new method for expanding entity related queries that automatically filters, weights and ranks candidate expasion terms extracted from Wikipedia articles related to the original query. Our method is based on state-of-the-art tag recommendation methods that exploit heuristic metrics to estimate the descriptive capacity of a given term. Originally proposed for the context of tags, we here apply these recommendation methods to weight and rank terms extracted from multiple fields of Wikipedia articles according to their relevance for the article. We evaluate our method comparing it against three state-of-the-art baselines in three collections. Our results indicate that our method outperforms all baselines in all collections, with relative gains in MAP of up to 14% against the best ones.