CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
WordsEye: an automatic text-to-scene conversion system
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
The Automatic Interpretation of Nominalizations
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Noun classification from predicate-argument structures
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A corpus-based account of regular polysemy: the case of context-sensitive adjectives
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Automatic text-to-scene conversion in the traffic accident domain
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Information extraction to generate visual simulations of car accidents from written descriptions
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartI
Frame semantics in text-to-scene generation
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part IV
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
VigNet: grounding language in graphics using frame semantics
RELMS '11 Proceedings of the ACL 2011 Workshop on Relational Models of Semantics
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part IV
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There has been a great deal of work over the past decade on inferring semantic information from text corpora. This paper is another instance of this kind of work, but is also slightly different in that we are interested not in extracting semantic information per se, but rather real-world knowledge. In particular, given a description of a particular action --- e.g. John was eating breakfast --- we want to know where John is likely to be, what time of day it is, and so forth. Humans on hearing this sentence would form a mental image that makes a lot of inferences about the environment in which this action occurs: they would probably imagine someone in their kitchen in the morning, perhaps in their dining room, seated at a table, eating a meal.We propose a method that makes use of Dunning's likelihood ratios to extract from text corpora strong associations between particular actions and locations or times when those actions occur. We also present an evaluation of the method. The context of this work is a text-to-scene conversion system called WordsEye, where in order to depict an action such as John was eating breakfast, it is desirable to make reasonable inferences about where and when that action is taking place so that the resulting picture is a reasonable match to one's mental image of the action.