A method and an apparatus for constructing an object relationship network and an electronic device are provided by the present disclosure, relating to the field of artificial intelligence technologies, such as deep neural networks, deep learning, etc. A specific implementation solution is: extracting keywords in respective text contents corresponding to a plurality of objects to obtain keywords corresponding to respective objects; and according to the keywords corresponding to the objects, a similarity between the plurality of objects is determined; and then according to the similarity between the plurality of objects, an object relationship network between the plurality of objects is constructed. Since the object relationship network constructed by means of the similarity between the plurality of objects can accurately describe a closeness degree of a relationship between the objects, thus, the plurality of objects can be managed effectively by means of the constructed object relationship network.

To prevent intent classifiers from potentially choosing intents that are ineligible for the current input due to policies, dynamic intent classification systems and methods are provided that dynamically control the possible set of intents using environment variables (also referred to as external variables). Associations between environment variables and ineligible intents, referred to as culling rules, are used.

To prevent intent classifiers from potentially choosing intents that are ineligible for the current input due to policies, dynamic intent classification systems and methods are provided that dynamically control the possible set of intents using environment variables (also referred to as external variables). Associations between environment variables and ineligible intents, referred to as culling rules, are used.

A computer system configured for autonomous learning of entity values is provided. The computer system includes a memory that stores associations between entities and fields of response data. The computer system also includes a processor configured to receive a request to process an intent; generate a request to fulfill the intent; transmit the request to a fulfillment service; receive, from the fulfillment service, response data specifying values of the fields; identify the values of the fields within the response data; identify the entities via the associations using the fields; store, within the memory, the values of the fields as values of the entities; and retrain a natural language processor using the values of the entities.

A computer system configured for autonomous learning of entity values is provided. The computer system includes a memory that stores associations between entities and fields of response data. The computer system also includes a processor configured to receive a request to process an intent; generate a request to fulfill the intent; transmit the request to a fulfillment service; receive, from the fulfillment service, response data specifying values of the fields; identify the values of the fields within the response data; identify the entities via the associations using the fields; store, within the memory, the values of the fields as values of the entities; and retrain a natural language processor using the values of the entities.

A natural language processing method, comprising: receiving multiple input words; and reducing the multiple words into one or more subject word data structures according to sets stored in a database, wherein one of the subject word data structures includes a first input word and a second input words among the input words, wherein one of the sets includes a compatible relation between the first input word and the second input word, wherein the compatible relation between the first input word and the second word includes a compatible property for denoting an intensity representing occurrences of the first input word and the second word in a training corpus.

A natural language processing method, comprising: receiving multiple input words; and reducing the multiple words into one or more subject word data structures according to sets stored in a database, wherein one of the subject word data structures includes a first input word and a second input words among the input words, wherein one of the sets includes a compatible relation between the first input word and the second input word, wherein the compatible relation between the first input word and the second word includes a compatible property for denoting an intensity representing occurrences of the first input word and the second word in a training corpus.

A chat input identifier may receive various chat inputs based on voice or text inputs from a user. The chat input identifier may apply different filters to the chat inputs to identify one or more chat correction pairs (e.g., chat input with errors, corrected chat input) from among the plurality of chat inputs. The chat correction pairs are used to train an auto-correction model. The trained auto-correction model receives a given chat input that has one or more errors. The auto-correction model processes the given chat input to generate a corrected version of the given chat input (without the need to obtain a correction from the user). The corrected chat input is then provided to a dialog-driven application.

An electronic device is provided. The electronic device includes processor, and a memory that stores instructions. The instructions, when executed by the processor, cause the electronic device to receive a user input, to identify a natural language input corresponding to the user input, to identify a first natural language output corresponding to the natural language input, to identify at least one specified word from at least one word included in the first natural language output, to identify a second natural language output based on a fact that the at least one specified word is identified, and to output the second natural language output such that the second natural language output is provided to a user.

A method of content production (e.g., content editing) using content modeling to facilitate content production. In one embodiment, an automated process is configured to render content. For a given content portion, and as the given portion is being rendered, the portion is processed to generate a content model. With respect to a concept expressed in or otherwise associated with the content, the system compares the content model with a target content derived model to generate a relevancy score. The target content derived model is generated by (a) identifying a set of target content portions in which the concept is expressed, (b) generating from each content portion an associated target content model; and (c) performing a vector operation on the associated target content models. Preferably, each associated target content model is built using an Artificial Intelligence (AI)-based content analysis. The relevancy score is used to generate a content production recommendation.