Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating application development. In embodiments, input is received from a designer, the input including an indication of a class of applications and one or more preferences for designing a module specification. Thereafter, the module specification is created in association with the class of applications. Input is received from a teacher in association with the module specification. At least a portion of such input indicates how to recognize when a user intends to run an application. An application is generated based on the input provided from the teacher. Such an application can be used by one or more users.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating application development. In embodiments, input is received from a designer, the input including an indication of a class of applications and one or more preferences for designing a module specification. Thereafter, the module specification is created in association with the class of applications. Input is received from a teacher in association with the module specification. At least a portion of such input indicates how to recognize when a user intends to run an application. An application is generated based on the input provided from the teacher. Such an application can be used by one or more users.

Abstract: The described implementations relate to natural language processing, and more particularly to training a language prior model using a model structure. The language prior model can be trained using parameterized representations of lexical structures such as training sentences, as well as parameterized representations of lexical units such as words or n-grams. During training, the parameterized representations of the lexical structures and the lexical units can be adjusted using the model structure. When the language prior model is trained, the parameterized representations of the lexical structures can reflect how the lexical units were used in the lexical structures.

Abstract: The described implementations relate to natural language processing, and more particularly to training a language prior model using a model structure. The language prior model can be trained using parameterized representations of lexical structures such as training sentences, as well as parameterized representations of lexical units such as words or n-grams. During training, the parameterized representations of the lexical structures and the lexical units can be adjusted using the model structure. When the language prior model is trained, the parameterized representations of the lexical structures can reflect how the lexical units were used in the lexical structures.

Abstract: Methods, systems, and apparatus for generation, distribution and verification of tokens are described. In an implementation, a method is described in which a value of an offer is determined and a token for representing the offer is generated. The token has a number of characters based on the determination of the value of the offer.

Abstract: Relevance of advertisements provided in response to a query is improved based on locations associated with the query and the advertisement. A confidence level can optionally be determined for associated locations. The associated locations can be used to calculate a geographic distance between a query location and an advertisement location. A relevance can be determined for the advertisements based in part on the calculated geographic distances. The associated locations can also be compared to determine stacking and barrier relationships between locations.

Abstract: Methods, systems, and apparatus for generation, distribution and verification of tokens are described. In an implementation, a method is described in which a value of an offer is determined and a token for representing the offer is generated. The token has a number of characters based on the determination of the value of the offer.

Abstract: Methods, systems, and apparatus for generation, distribution and verification of tokens are described. In an implementation, a method is described in which a value of an offer is determined and a token for representing the offer is generated. The token has a number of characters based on the determination of the value of the offer.