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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 – 19 of 19 1 Logical validation, answer merging and witness selection: A study in multi-stream question answering Ingo Glöckner; Sven Hartrumpf; Johannes Leveling In: http://pi7.fernuni-hagen.de/papers/gloeckner_hartrumpf_etal07.pdf (2007) BASE Show details 2 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 3 The Role of Intensional and Extensional Interpretation in Semantic Representations -- The Intensional and Preextensional Layers in MultiNet Hermann Helbig; Ingo Glöckner In: http://pi7.fernuni-hagen.de/gloeckner/nlpcs07.pdf (2007) BASE Show details 4 Logical Validation, Answer Merging and Witness Selection A Study in Multi-Stream Question Answering Ingo Glöckner; Sven Hartrumpf; Johannes Leveling In: http://riao.free.fr/papers/65.pdf (2007) BASE Show details 5 Automatic knowledge acquisition by semantic analysis and assimilation of textual information Ingo Glöckner; Sven Hartrumpf; Hermann Helbig In: http://pi7.fernuni-hagen.de/gloeckner/konvens06-assim.pdf (2006) BASE Show details 6 Answer validation through robust logical inference Ingo Glöckner In: http://pi7.fernuni-hagen.de/gloeckner/glocknerCLEF2006.pdf (2006) BASE Show details 7 Fuzzy Quantifiers: A Natural Language Technique for Data Fusion Ingo Glöckner; Alois Knoll In: http://sunknoll1.informatik.tu-muenchen.de/~knoll/publikationen/fusion2001.pdf (2001) BASE Show details 8 Fuzzy Quantifiers for Data Summarization and their Role in Granular Computing Ingo Glöckner; Alois Knoll In: http://www6.in.tum.de/~knoll/publikationen/ifsa2001.pdf (2001) BASE Show details 9 A formal theory of fuzzy natural language quantification and its role in granular computing Ingo Glöckner; Alois Knoll In: http://sunknoll1.informatik.tu-muenchen.de/~knoll/publikationen/pchapter.pdf (2001) BASE Show details 10 An Axiomatic Theory of Fuzzy Quantifiers in Natural Languages Ingo Glöckner In: http://kassandra.techfak.uni-bielefeld.de/ingo/tr2k3.pdf (2000) BASE Show details 11 A Framework for Evaluating Approaches to Fuzzy Quantification Ingo Glöckner In: http://www.techfak.uni-bielefeld.de/techfak/ags/ti/forschung/publikationen/tr99-03.ps.gz (1999) BASE Show details 12 A Framework for Evaluating Fusion Operators Based on the Theory of Generalized Quantifiers Ingo Glöckner; Alois Knoll In: http://www.techfak.uni-bielefeld.de/techfak/ags/ti/personen/ingo/mfi99.ps.gz (1999) BASE Show details 13 University of Hagen at CLEF 2008: Answer Validation Exercise Ingo Glöckner In: http://www.clef-campaign.org/2008/working_notes/glockner-AVE-paperCLEF2008.pdf Abstract: RAVE (Real-time Answer Validation Engine) is a logic-based answer validator/selector designed for application in real-time question answering. RAVE uses the same toolchain for deep linguistic analysis and the same background knowledge as its predecessor (MAVE), which took part in the AVE 2007. However, a full logical answer check as in MAVE was not considered suitable for real-time answer validation since it requires parsing of all answer candidates. Therefore RAVE uses a simplified validation model where the prover only checks if the support passage contains a correct answer at all. This move from logic-based answer validation to logical validation of supporting snippets permits RAVE to avoid any parsing of answers, i.e. the system only needs a parse of the question and pre-computed snippet analyses. In this way very low validation/selection times can be achieved. Machine learning is used for assigning local validation scores using both logic-based and shallow features. The resulting local validation scores are improved by aggregation. One of the key features of RAVE is its innovative aggregation model, which is robust against duplicated information in the support passages. In this model, the effect of aggregation is controlled by the lexical diversity of the support passages for a given answer. If the support passages have no terms in common, then the aggregation has maximal effect and the passages URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.153.2993 http://www.clef-campaign.org/2008/working_notes/glockner-AVE-paperCLEF2008.pdf BASE Hide details 14 University of Hagen at CLEF 2007: Answer Validation Exercise Ingo Glöckner In: http://www.clef-campaign.org/2007/working_notes/glocknerclef2007.pdf BASE Show details 15 The LogAnswer Project at ResPubliQA 2010 Ingo Glöckner; Björn Pelzer In: http://clef2010.org/resources/proceedings/clef2010labs_submission_30.pdf BASE Show details 16 Optimal Selection of Proportional Bounding Quantifiers in Linguistic Data Summarization Ingo Glöckner In: http://pi7.fernuni-hagen.de/gloeckner/smps06-gloeckner.pdf BASE Show details 17 Towards Logic-Based Question Answering under Time Constraints Ingo Glöckner In: http://www.iaeng.org/publication/IMECS2008/IMECS2008_pp13-18.pdf BASE Show details 18 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 19 Combining Theorem Proving with Natural Language Processing Björn Pelzer; Ingo Glöckner In: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-373/paper-06.pdf BASE Show details

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