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Predicting reading difficulty with statistical language models.

, and . J. Assoc. Inf. Sci. Technol., 56 (13): 1448-1462 (2005)

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Tutorial summary: Machine learning in IR: recent successes and new opportunities., , and . ICML, volume 382 of ACM International Conference Proceeding Series, page 17. ACM, (2009)Shame to be sham: addressing content-based grey hat search engine optimization., , and . SIGIR, page 1013-1016. ACM, (2013)The effect of document retrieval quality on factoid question answering performance., , , and . SIGIR, page 574-575. ACM, (2004)Query expansion using random walk models., and . CIKM, page 704-711. ACM, (2005)Optimizing search results for human learning goals., and . Inf. Retr. Journal, 20 (5): 506-523 (2017)Characterizing web content, user interests, and search behavior by reading level and topic., , , and . WSDM, page 213-222. ACM, (2012)A Language Modeling Approach to Predicting Reading Difficulty., and . HLT-NAACL, page 193-200. The Association for Computational Linguistics, (2004)Initial Results with Structured Queries and Language Models on Half a Terabyte of Text., , and . TREC, volume 500-261 of NIST Special Publication, National Institute of Standards and Technology (NIST), (2004)Copulas for information retrieval., , and . SIGIR, page 663-672. ACM, (2013)A unified optimization framework for robust pseudo-relevance feedback algorithms., and . CIKM, page 1069-1078. ACM, (2010)