Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Automatic labeling of semantic roles
Computational Linguistics
Enabling domain experts to convey questions to a machine: a modified, template-based approach
Proceedings of the 2nd international conference on Knowledge capture
Using transformations to improve semantic matching
Proceedings of the 2nd international conference on Knowledge capture
Matching utterances to rich knowledge structures to acquire a model of the speaker's goal
Proceedings of the 3rd international conference on Knowledge capture
Indirect anaphora resolution as semantic path search
Proceedings of the 3rd international conference on Knowledge capture
PageRank on semantic networks, with application to word sense disambiguation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Capturing and answering questions posed to a knowledge-based system
Proceedings of the 4th international conference on Knowledge capture
Interpreting loosely encoded questions
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A unified knowledge based approach for sense disambiguationm and semantic role labeling
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning noun-modifier semantic relations with corpus-based and WordNet-based features
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning by reading: a prototype system, performance baseline and lessons learned
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
The knowledge required to interpret noun compounds
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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An important problem in artificial intelligence is capturing, from natural language, formal representationsallthat can be used by a reasoner to compute an answer. Many researchers have studied this problem by developing algorithms addressing specific phenomena in natural language interpretation, but few have studied (or cataloged) the types of failures associated with this problem. Knowledgeallof these failures can help researchers by providing a roadallmap of open research problems and help practitioners by providing a checklist of issues to address in order to build systems that can achieve good performance on this problem.allIn this paper, we present a study -- conducted in the context of the Halo Project -- cataloging the types of failures that occur when capturing knowledge from naturallanguage. We identified the categories of failures by examining a corpus of questions posed byallnaive usersallto a knowledge based question answering system and empirically demonstrated the generality of ourallcategorizations. We also describe available technologies that can address some of the failures we have identified.