Feature structures and nonmonotonicity
Computational Linguistics - Special issue on inheritance: I
Skeptical and credulous default unification with applications to templates and inheritance
Inheritance, defaults and the lexicon
Defaults in lexical representation
Inheritance, defaults and the lexicon
Functional Pearl trouble shared is trouble halved
Haskell '03 Proceedings of the 2003 ACM SIGPLAN workshop on Haskell
Default representation in constraint-based frameworks
Computational Linguistics
Priority union and generalization in discourse grammars
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Lenient default unification for robust processing within unification based grammar formalisms
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Less is more: using a single knowledge representation in dialogue systems
HLT-NAACL-TEXTMEANING '03 Proceedings of the HLT-NAACL 2003 workshop on Text meaning - Volume 9
An Anytime Algorithm for Computing Inconsistency Measurement
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Managing information fusion with formal concept analysis
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
The maximum clique enumeration problem: algorithms, applications and implementations
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
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Default unification operations combine strict information with information from one or more defeasible feature structures. Many such operations require finding the maximal subsets of a set of atomic constraints that are consistent with each other and with the strict feature structure, where a subset is maximally consistent with respect to the subsumption ordering if no constraint can be added to it without creating an inconsistency. Although this problem is NP-complete, there are a number of heuristic optimizations that can be used to substantially reduce the size of the search space. In this article, we propose a novel optimization, leaf pruning, which in some cases yields an improvement in running time of several orders of magnitude over previously described algorithms. This makes default unification efficient enough to be practical for a wide range of problems and applications.