A system for selecting an image to accompany text from a user in connection with a social media post. The system is capable of receiving text from the user, identifying one or more search terms based on the text, identifying candidate images from images in one or more image databases using the search terms, presenting one or more candidate images to the user, receiving from the user a selected image from the one or more candidate images, generating the social media post comprising the selected image and the user-submitted text, and transmitting the social media post for display.

Techniques for selecting an emotion type code associated with semantic content in an interactive dialog system. In an aspect, fact or profile inputs are provided to an emotion classification algorithm, which selects an emotion type based on the specific combination of fact or profile inputs. The emotion classification algorithm may be rules-based or derived from machine learning. A previous user input may be further specified as input to the emotion classification algorithm. The techniques are especially applicable in mobile communications devices such as smartphones, wherein the fact or profile inputs may be derived from usage of the diverse function set of the device, including online access, text or voice communications, scheduling functions, etc.

A system, method and computer-readable storage devices are for processing natural language commands, such as commands to a robotic arm, using a Tag & Parse approach to semantic parsing. The system first assigns semantic tags to each word in a sentence and then parses the tag sequence into a semantic tree. The system can use statistical approach for tagging, parsing, and reference resolution. Each stage can produce multiple hypotheses, which are re-ranked using spatial validation. Then the system selects a most likely hypothesis after spatial validation, and generates or outputs a command. In the case of a robotic arm, the command is output in Robot Control Language (RCL).

In one example of the disclosure, a machine-translation for each of a plurality of strings is determined, the strings for display upon execution of a subject application. A first display of a test step to be performed by a test application during execution of the subject application is caused. A second display of a state for the subject application that includes the plurality of strings is caused concurrent with the first display. A user-translation for each of the strings is obtained, the user-translations provided via a GUI included within the second display. A translation property file associated with the subject application is amended to include the user-translations.

The present invention discloses a state machine based context-sensitive multi-round dialog management system, comprising: an input module, for receiving multi-modal input information from a user; an intention identification engine module, for identifying intention information in the multi-modal input information; an intention module, for bringing multiple intention information identified by the intention identification engine module into one-to-one correspondence with multiple intention sub-modules at back ends; a state machine module, comprising a plurality of state machines for managing a relevant context in the dialog management system and providing support for an output result; an instruction parsing engine module, comprising a plurality of instruction parsing engine sub-modules for parsing corresponding intention information and acquiring the parsed multiple intention information; and an output module, for acquiring policy information according to the results from the parsing engine module and the intention identification module, and transmitting the policy information to the state machine module.

A corpus generation device and method, the device comprising: a segmentation module, connected to at least one monolingual parallel corpus for segmenting a sentence into words and processing the segmented words by a knowledge-driven approach; a classification module, for classifying sentences having different tag sequences but the same meaning into the same sentence cluster; a mapping module, for determining the categories of sentence structures of all the sentences in the sentence cluster, recording and storing a mapping mode for transforming tags between sentence structures when different categories of sentence structures in the same sentence cluster are transformed; a sentence structure generation module, for generating sentence structures according to a first mapping mode between a first category of sentence structures in one of the sentence clusters and other categories of sentence structures in the same sentence cluster; and a corpus generation module, for nesting a word corresponding to a sequence tag to generate a new monolingual parallel corpus.

A method is provided for creating a lexical tree from a statement in a natural language. The method is implemented by a natural-language processing module. The method includes: receiving a statement in natural language in the form of a string of characters; iteratively processing the statement as a function of at least one processing parameter and one ontological dictionary, delivering at least one relational graph corresponding to at least one lexical item included in the statement in natural language; and creating a data structure at output having all possible combinations of the lexical items of the statement in natural language on the basis of the at least one relational graph.

An example method involves (i) accessing first data representing text, wherein the text defines at least one position representing a particular type of grammatical break between two portions of the text; (ii) identifying, from among the at least one position, a position that is closest to a target position within the text; (iii) based on the identified position within the text, generating second data that represents a proper subset of the text, wherein the proper subset extends from an initial position within the text to the identified position within the text; and (iv) providing output based on the generated second data.

A server accesses a natural language query corresponding to one of a plurality of natural languages. The server maps, using a query-to-vector engine configured to leverage word embeddings in each of the plurality of natural languages to map natural language queries in the plurality of natural languages to vectors corresponding to meanings of the natural language queries, the natural language query to a vector. The server matches the vector to an intent representing a prediction associated with the natural language query. The server provides a response to the natural language query based on the intent.

A server accesses a natural language query corresponding to one of a plurality of natural languages. The server maps, using a query-to-vector engine configured to leverage word embeddings in each of the plurality of natural languages to map natural language queries in the plurality of natural languages to vectors corresponding to meanings of the natural language queries, the natural language query to a vector. The server matches the vector to an intent representing a prediction associated with the natural language query. The server provides a response to the natural language query based on the intent.