Inferring the environment in a text-to-scene conversion system

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.