Abstract: In some examples, a smart container may enable crowdsourced delivery of items from a pickup location to a delivery location. The container may include a location sensor and a communication interface to enable the container to provide its location to at least one of a service computing device, a courier device, a sender device, or a recipient device. In some cases, a service provider may employ public transport vehicles to transport the container along a portion of a delivery route. One crowdsourced courier may pick up the container for placement on the public transport vehicle and another crowdsourced courier may pick up the container from the public transport vehicle and deliver the container to the delivery location. Further, the container may include multiple compartments, and recipients who receive a shipment in the container may be solicited to deliver the container to a delivery location of a next recipient.

Abstract: Content data items in a first electronic file that correspond to entities in a database comprising categorized entities are identified. Modified content data items of a second electronic file are generated, the modified content data items indicating how the content data items are to be interpreted by a natural language query answering system. Information in the second electronic file indicating how the content data items are to be interpreted by the natural language query answering system are modified in response to user input. The second electronic file is used by the natural language query answering system to facilitate analysis of a natural language query regarding content in the first electronic file. Results of the analysis using the natural language query answering system are output.

Abstract: Content data items in a first electronic file that correspond to entities in a database comprising categorized entities are identified. Modified content data items of a second electronic file are generated, the modified content data items indicating how the content data items are to be interpreted by a natural language query answering system. Information in the second electronic file indicating how the content data items are to be interpreted by the natural language query answering system are modified in response to user input. The second electronic file is used by the natural language query answering system to facilitate analysis of a natural language query regarding content in the first electronic file. Results of the analysis using the natural language query answering system are output.

Abstract: A natural language query answering system includes an external data pre-processor configured to i) analyze a first data set, the first data set received via a communication network, to identify data in the first data set that corresponds to entities in a database comprising categorized entities, and ii) generate a second data set corresponding to the first data set and based on the analysis of the first data set, wherein the second data set includes information indicating how data in the second data set is to be interpreted by the natural language query answering system. The natural language query answering system is configured to retrieve data and/or process data from the second data set in response to natural language queries.

Abstract: In a method for generating example queries, an actual query is received, and the actual query is portioned into one or more tokens. One or more fragments of the actual query are generated based on reassembly of the one or more tokens. A plurality of example queries are generated based on different interpretations of one or more fragments, and at least one example query is outputted.

Abstract: A natural language query answering system includes an external data pre-processor configured to i) analyze a first data set, the first data set received via a communication network, to identify data in the first data set that corresponds to entities in a database comprising categorized entities, and ii) generate a second data set corresponding to the first data set and based on the analysis of the first data set, wherein the second data set includes information indicating how data in the second data set is to be interpreted by the natural language query answering system. The natural language query answering system is configured to retrieve data and/or process data from the second data set in response to natural language queries.