Artificial Intelligence - Special volume on natural language processing
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Some challenges and grand challenges for computational intelligence
Journal of the ACM (JACM)
Computational Linguistics
Discovery of inference rules for question-answering
Natural Language Engineering
ITP: description of the Interpretext system as used for MUC-3
MUC3 '91 Proceedings of the 3rd conference on Message understanding
On-demand information extraction
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
A global joint model for semantic role labeling
Computational Linguistics
Proceedings of the 4th International Workshop on Semantic Evaluations
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Coupling semi-supervised learning of categories and relations
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
Prediction and substantiation: two processes that comprise understanding
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
When did that happen?: linking events and relations to timestamps
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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This paper explores the close relationship between question answering and machine reading, and how the active use of reasoning to answer (and in the process, disambiguate) questions can also be applied to reading declarative texts, where a substantial proportion of the text's contents is already known to (represented in) the system. In question answering, a question may be ambiguous, and it may only be in the process of trying to answer it that the "right" way to disambiguate it becomes apparent. Similarly in machine reading, a text may be ambiguous, and may require some process to relate it to what is already known. Our conjecture in this paper is that these two processes are similar, and that we can modify a question answering tool to help "read" new text that augments existing system knowledge. Specifically, interpreting a new text T can be recast as trying to answer, or partially answer, the question "Is it true that T?", resulting in both appropriate disambiguation and connection of T to existing knowledge. Some preliminary investigation suggests this might be useful for proposing knowledge base extensions, extracted from text, to a knowledge engineer.