31. An automated information processing method, comprising: processing inferences about a probability distribution over informational goals given a query, a physical location of a user, and parts of speech containing a focus of attention of the query in accordance with attributes of a user and most appropriate level of detail, wherein the probability distribution is stored in one or more data structures that are comprised in an inference model, wherein parsing the query into the parts of speech facilitates accessing the data structures in the inference model; employing the inferences in at least one of a post-filter process, a reformulation process, a process for dynamically crafting an answer to the query; and a process for driving a dialog in pursuit of refining the probability distribution, and a process for driving dialog in pursuit of a more appropriate query in order to satisfy the informational goals before crafting the answer when at least one of the inferences about the informational goals has a likelihood below a predefined probability threshold wherein one or more of at least an attribute associated with the user or the inference model is refined based upon occurrence of the dialog with the user.
31. An automated information processing method, comprising: processing inferences about a probability distribution over informational goals given a query, a physical location of a user, and parts of speech containing a focus of attention of the query in accordance with attributes of a user and most appropriate level of detail, wherein the probability distribution is stored in one or more data structures that are comprised in an inference model, wherein parsing the query into the parts of speech facilitates accessing the data structures in the inference model; employing the inferences in at least one of a post-filter process, a reformulation process, a process for dynamically crafting an answer to the query; and a process for driving a dialog in pursuit of refining the probability distribution, and a process for driving dialog in pursuit of a more appropriate query in order to satisfy the informational goals before crafting the answer when at least one of the inferences about the informational goals has a likelihood below a predefined probability threshold wherein one or more of at least an attribute associated with the user or the inference model is refined based upon occurrence of the dialog with the user. 33. The method of claim 31 , the attributes are inferred from at least one of a history of queries or profile information about the user.