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    • Johannes Leveling (7)
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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 University of Hagen at QA@CLEF 2007: Coreference Resolution for Questions and Answer Merging Sven Hartrumpf; Ingo Glöckner; Johannes Leveling In: http://www.clef-campaign.org/2007/working_notes/hartrumpfCLEF2007.pdf (2007) BASE Show details 2 Document Expansion, Query Translation and Language Modeling Johannes Leveling; Dong Zhou; Gareth F. Jones... In: http://www.clef-campaign.org/2009/working_notes/leveling-paperCLEF2009.pdf BASE Show details 3 Efficient question answering with question decomposition and multiple answer streams Sven Hartrumpf; Johannes Leveling In: http://pi7.fernuni-hagen.de/gloeckner/clef08-fuha-wn.pdf BASE Show details 4 Query Recovery of Short User Queries: On Query Expansion with Stopwords Johannes Leveling; Gareth J. F. Jones In: http://www.cngl.ie/drupal/sites/default/files/papers3/Query%20Recovery%20of%20Short%20User%20Queries.pdf Abstract: User queries to search engines are observed to predominantly contain inflected content words but lack stopwords and capitalization. Thus, they often resemble natural language queries after case folding and stopword removal. Query recovery aims to generate a linguistically well-formed query from a given user query as input to provide natural language processing tasks and cross-language information retrieval (CLIR). The evaluation of query translation shows that translation scores (NIST and BLEU) decrease after case folding, stopword removal, and stemming. A baseline method for query recovery reconstructs capitalization and stopwords, which considerably increases translation scores and significantly increases mean average precision for a standard CLIR task. Categories and Subject Descriptors: Keyword: CLIR; Experimentation; H.3.3 [INFORMATION STORAGE AND RETRIEVAL] Information Search and Retrieval—Query formulation; Measurement Keywords; Performance; Query Expansion; Query Reformulation; Search process General Terms URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.188.2536 http://www.cngl.ie/drupal/sites/default/files/papers3/Query%20Recovery%20of%20Short%20User%20Queries.pdf BASE Hide details 5 Indexing—Linguistic processing Johannes Leveling; Walid Magdy; Gareth J. F. Jones In: http://doras.dcu.ie/16447/1/An_Investigation_of_Decompounding_for_Cross-Language_Patent_Search.pdf BASE Show details 6 Efficient Question Answering with Question Decomposition and Multiple Answer Streams Sven Hartrumpf; Ingo Glöckner; Johannes Leveling In: http://www.clef-campaign.org/2008/working_notes/hartrumpf-paperCLEF2008.pdf BASE Show details 7 University of Hagen at GeoCLEF 2008: Combining IR and QA for Geographic Information Retrieval Johannes Leveling; Sven Hartrumpf In: http://www.clef-campaign.org/2008/working_notes/leveling-paperCLEF2008.pdf BASE Show details

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