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    • Content Analysis and Indexing—Linguistic processing H.3.3 [Information Storage and Retrieval (3)
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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 – 3 of 3 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 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 Abstract: The German question answering (QA) system IRSAW (formerly: InSicht) participated in QA@CLEF for the fifth time. IRSAW was introduced in 2007, by integrating the deep an-swer producer InSicht, several shallow answer producers, and a logical validator. InSicht realizes a deep QA approach: it transforms documents to semantic representations using a parser, draws inferences on semantic representations with rules, and matches semantic representations derived from questions and documents. InSicht was improved for QA@CLEF 2008 mainly in the following areas. The coreference resolver was trained on question series instead of newspaper texts in order to be better applicable for follow-up questions in question series. Questions are decomposed by several methods on the level of semantic representations. On the shallow processing side, the number of answer producers was increased from 2 to 4, by adding FACT and SHASE. The answer validator introduced in the previous year was replaced with the faster RAVE validator designed for logic-based answer validation under time constraints. Using RAVE for merging the results of the answer producers, monolingual German runs and bilingual runs with Keyword: Categories and Subject Descriptors H.3.1 [Information Storage and Retrieval; Content Analysis and Indexing—Linguistic processing H.3.3 [Information Storage and Retrieval; Coref- erence resolution; Deep semantic processing of questions and documents; Follow-up questions; Information Search and Retrieval—Search process H.3.4 [Information Storage and Retrieval; Knowledge Representation Formalisms and Methods—Semantic networks I.2.7 [Artificial Intelligence; Measurement; Natural Language Processing—Language parsing and understanding General Terms Experimentation; Performance Keywords Question answering; Questi; Systems and Software—Performance evaluation I.2.4 [Artificial Intelligence URL: http://pi7.fernuni-hagen.de/gloeckner/clef08-fuha-wn.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.644.2478 BASE Hide details 3 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

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