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Kevin Knight [5]Kevin M. Knight [1]
  1. Measuring inconsistency.Kevin Knight - 2002 - Journal of Philosophical Logic 31 (1):77-98.
    I provide a method of measuring the inconsistency of a set of sentences from 1-consistency, corresponding to complete consistency, to 0-consistency, corresponding to the explicit presence of a contradiction. Using this notion to analyze the lottery paradox, one can see that the set of sentences capturing the paradox has a high degree of consistency (assuming, of course, a sufficiently large lottery). The measure of consistency, however, is not limited to paradoxes. I also provide results for general sets of sentences.
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  2.  10
    Summarization beyond sentence extraction: A probabilistic approach to sentence compression.Kevin Knight & Daniel Marcu - 2002 - Artificial Intelligence 139 (1):91-107.
  3.  39
    Two information measures for inconsistent sets.Kevin M. Knight - 2003 - Journal of Logic, Language and Information 12 (2):227-248.
    I present two measures of information for both consistentand inconsistent sets of sentences in a finite language ofpropositional logic. The measures of information are based onmeasures of inconsistency developed in Knight (2002).Relative information measures are then provided corresponding to thetwo information measures.
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  4.  4
    Discovering the linear writing order of a two-dimensional ancient hieroglyphic script.Shou de Lin & Kevin Knight - 2006 - Artificial Intelligence 170 (4-5):409-421.
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    Fast and optimal decoding for machine translation.Ulrich Germann, Michael Jahr, Kevin Knight, Daniel Marcu & Kenji Yamada - 2004 - Artificial Intelligence 154 (1-2):127-143.
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    Probabilistic Entailment and a Non-Probabilistic Logic.Kevin Knight - 2003 - Logic Journal of the IGPL 11 (3):353-365.
    In this paper we present a probabilistic notion of entailment for finite sets of premises, which has classical entailment as a special case, and show that it is well defined; i.e., that the problem of whether a sentence is entailed by a set of premises is computable. Further we present a natural deductive system and prove that it is the strongest deductive system possible without referring to probabilities.
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