Abstract: A method, system and program product 100 usable by domain developers having any experience level in creating domain models. A representation of domain model knowledge is derived from a domain specification. The domain specification includes multiple potential domain objects, e.g., tables of APIs functional arguments, and each of the potential domain objects include one or more attributes. Potential domain objects are selected one at a time 102 from the specification and offered to the developer. The developer decides 104 whether or not to include the potential domain object in the domain model. If the developer decides to include the potential domain object 106, then the system provides a default name 108, i.e., the table name or argument name, and allows the developer to rename the selected domain object 110. Then, after having selected the object, potential attributes 112, e.g., table columns 1122, are selected from the object and offered to the developer 116.

Abstract: A method (900), device (200) and system (100) provide, in response to text/linguistic input, one of a set of pre-determined meanings which is the most likely intended meaning of that input. A trained meaning discriminator is generated from an annotated training corpus and a meaning discriminator trainer. The trained meaning discriminator generates a meaning vector from an input utterance. The intended meaning encoder analyzes the meaning vector to determine the most likely intended meaning and confidence measures.