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1
Logical validation, answer merging and witness selection: A study in multi-stream question answering
In: http://pi7.fernuni-hagen.de/papers/gloeckner_hartrumpf_etal07.pdf (2007)
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2
University of Hagen at QA@CLEF 2007: Coreference Resolution for Questions and Answer Merging
In: http://www.clef-campaign.org/2007/working_notes/hartrumpfCLEF2007.pdf (2007)
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3
The Role of Intensional and Extensional Interpretation in Semantic Representations -- The Intensional and Preextensional Layers in MultiNet
In: http://pi7.fernuni-hagen.de/gloeckner/nlpcs07.pdf (2007)
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4
Logical Validation, Answer Merging and Witness Selection A Study in Multi-Stream Question Answering
In: http://riao.free.fr/papers/65.pdf (2007)
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5
Automatic knowledge acquisition by semantic analysis and assimilation of textual information
In: http://pi7.fernuni-hagen.de/gloeckner/konvens06-assim.pdf (2006)
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6
Answer validation through robust logical inference
In: http://pi7.fernuni-hagen.de/gloeckner/glocknerCLEF2006.pdf (2006)
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7
Fuzzy Quantifiers: A Natural Language Technique for Data Fusion
In: http://sunknoll1.informatik.tu-muenchen.de/~knoll/publikationen/fusion2001.pdf (2001)
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8
Fuzzy Quantifiers for Data Summarization and their Role in Granular Computing
In: http://www6.in.tum.de/~knoll/publikationen/ifsa2001.pdf (2001)
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9
A formal theory of fuzzy natural language quantification and its role in granular computing
In: http://sunknoll1.informatik.tu-muenchen.de/~knoll/publikationen/pchapter.pdf (2001)
Abstract: Fuzzy quantification is a linguistic granulation technique capable of expressing the global characteristics of a collection of individuals, or a relation between individuals, through meaningful linguistic summaries. However, existing approaches to fuzzy quantification fail to provide convincing results in the important case of two-place quantification (e.g. “many blondes are tall”). We develop an axiomatic framework for fuzzy quantification which complies with a large number of linguistically motivated adequacy criteria. In particular, we present the first models of fuzzy quantification which provide an adequate account of the “hard ” cases of multiplace quantifiers, non-monotonic quantifiers, and non-quantitative quantifiers, and we show how the resulting operators can be efficiently implemented based on histogram computations.
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.125.2672
http://sunknoll1.informatik.tu-muenchen.de/~knoll/publikationen/pchapter.pdf
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10
An Axiomatic Theory of Fuzzy Quantifiers in Natural Languages
In: http://kassandra.techfak.uni-bielefeld.de/ingo/tr2k3.pdf (2000)
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11
A Framework for Evaluating Approaches to Fuzzy Quantification
In: http://www.techfak.uni-bielefeld.de/techfak/ags/ti/forschung/publikationen/tr99-03.ps.gz (1999)
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12
A Framework for Evaluating Fusion Operators Based on the Theory of Generalized Quantifiers
In: http://www.techfak.uni-bielefeld.de/techfak/ags/ti/personen/ingo/mfi99.ps.gz (1999)
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13
University of Hagen at CLEF 2008: Answer Validation Exercise
In: http://www.clef-campaign.org/2008/working_notes/glockner-AVE-paperCLEF2008.pdf
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14
University of Hagen at CLEF 2007: Answer Validation Exercise
In: http://www.clef-campaign.org/2007/working_notes/glocknerclef2007.pdf
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15
The LogAnswer Project at ResPubliQA 2010
In: http://clef2010.org/resources/proceedings/clef2010labs_submission_30.pdf
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16
Optimal Selection of Proportional Bounding Quantifiers in Linguistic Data Summarization
In: http://pi7.fernuni-hagen.de/gloeckner/smps06-gloeckner.pdf
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17
Towards Logic-Based Question Answering under Time Constraints
In: http://www.iaeng.org/publication/IMECS2008/IMECS2008_pp13-18.pdf
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18
Efficient Question Answering with Question Decomposition and Multiple Answer Streams
In: http://www.clef-campaign.org/2008/working_notes/hartrumpf-paperCLEF2008.pdf
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19
Combining Theorem Proving with Natural Language Processing
In: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-373/paper-06.pdf
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