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Web"],"published-print":{"date-parts":[[2012,11]]},"abstract":"<jats:p>A persisting challenge in the field of information retrieval is the vocabulary mismatch between a user\u2019s information need and the relevant documents. One way of addressing this issue is to apply query modeling: to add terms to the original query and reweigh the terms. In social media, where documents usually contain creative and noisy language (e.g., spelling and grammatical errors), query modeling proves difficult. To address this, attempts to use external sources for query modeling have been made and seem to be successful. In this article we propose a general generative query expansion model that uses external document collections for term generation: the External Expansion Model (EEM). The main rationale behind our model is our hypothesis that each query requires its own mixture of external collections for expansion and that an expansion model should account for this. For some queries we expect, for example, a news collection to be most beneficial, while for other queries we could benefit more by selecting terms from a general encyclopedia. EEM allows for query-dependent weighing of the external collections.<\/jats:p>\n          <jats:p>We put our model to the test on the task of blog post retrieval and we use four external collections in our experiments: (i) a news collection, (ii) a Web collection, (iii) Wikipedia, and (iv) a blog post collection. Experiments show that EEM outperforms query expansion on the individual collections, as well as the Mixture of Relevance Models that was previously proposed by Diaz and Metzler [2006]. Extensive analysis of the results shows that our naive approach to estimating query-dependent collection importance works reasonably well and that, when we use \u201coracle\u201d settings, we see the full potential of our model. We also find that the query-dependent collection importance has more impact on retrieval performance than the independent collection importance (i.e., a collection prior).<\/jats:p>","DOI":"10.1145\/2382616.2382621","type":"journal-article","created":{"date-parts":[[2012,12,4]],"date-time":"2012-12-04T20:10:57Z","timestamp":1354651857000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Exploiting External Collections for Query Expansion"],"prefix":"10.1145","volume":"6","author":[{"given":"Wouter","family":"Weerkamp","sequence":"first","affiliation":[{"name":"University of Amsterdam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krisztian","family":"Balog","sequence":"additional","affiliation":[{"name":"NTNU Trondheim"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maarten","family":"de Rijke","sequence":"additional","affiliation":[{"name":"University of Amsterdam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2012,11]]},"reference":[{"volume":"2997","volume-title":"Proceedings of the 26th European Conference on IR Research (ECIR\u201904)","author":"Amati G.","key":"e_1_2_1_1_1"},{"key":"e_1_2_1_2_1","unstructured":"Aquaint-2. 2007. http:\/\/trec.nist.gov\/data\/qa\/2007_qadata\/qa.07.guidelines.html#documents. 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Addison-Wesley Professional."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390334.1390399"},{"volume-title":"Proceedings of the 17th Text REtrieval Conference (TREC\u201908)","year":"2009","author":"Balog K.","key":"e_1_2_1_6_1"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390334.1390377"},{"volume-title":"Proceedings of the 18th REtrieval Conference (TREC\u201909)","author":"Cartright M.-A.","key":"e_1_2_1_8_1"},{"key":"e_1_2_1_9_1","unstructured":"ClueWeb09. 2009. http:\/\/www.lemurproject.org\/clueweb09.php\/. 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In Language Modeling for Information Retrieval Kluwer International Series on Information Retrieval Springer. Lafferty J. and Zhai C. 2003. Probabilistic relevance models based on document and query generation. In Language Modeling for Information Retrieval Kluwer International Series on Information Retrieval Springer.","DOI":"10.1007\/978-94-017-0171-6_1"},{"key":"e_1_2_1_29_1","unstructured":"Lancaster F. 1968. Information Retrieval Systems: Characteristics Testing and Evaluation. John Wiley & Sons New York. Lancaster F. 1968. Information Retrieval Systems: Characteristics Testing and Evaluation . John Wiley & Sons New York."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/383952.383972"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557077"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1645953.1645988"},{"key":"e_1_2_1_33_1","unstructured":"Macdonald C. and Ounis I. 2006. 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