Methods and system for item matching are described. In one embodiment, compatibility-based text for an item may be accessed. A compatibility identifier may be identified based on the compatibility-based text. The compatibility identifier may be associated with an item cluster. The compatibility identifier may be used to identify a plurality of matching items. A result may be provided based on identification of the plurality of matching items. Additional methods and systems are disclosed.

A third-party service may be used to assist entities in responding to requests of users by determining a suggested response to a received communication. The third party service may receive a request from a first entity, such as via an application programming interface request, that includes a message in a conversation. A conversation feature vector may be computed by processing the message with a first neural network. A suggested respond to the message may be determined by processing the conversation feature vector with a second neural network. The third-party service may then return the suggested response for use in the conversation. The third-party service may similarly be used to assist other entities in responding to requests of users.

A method for generating visual feedback based on a textual representation comprising obtaining and processing a textual representation, identifying at least one textual feature of the textual representation, assigning at least one feature value to the at least one textual feature, and generating visual feedback based on the textual representation. The generated visual feedback comprises at least one visual feature corresponding to the at least one textual feature. A system for generating visual feedback based on a textual representation, comprising a capturing subsystem configured to capture the textual representation, a processing subsystem configured to identify at least one textual feature and to generate visual feedback based on the textual representation, and a graphical user output configured to display the generated visual feedback. The visual feedback generated based on the textual representation comprises at least one visual feature corresponding to the at least one textual feature.

A method for identifying documents that are similar in content to an input document includes receiving a request for identifying similar documents from among a plurality of candidate documents, retrieving document classification attributes for the input document and the candidate documents, where the document classification attributes are document level attributes. The method also includes comparing the document classification attributes of the input document with classification attributes of the candidate documents to identify a subset of the candidate documents having matching document classification attributes, retrieving semantic entities from the input document and from candidate documents in the subset, pairwise comparing the semantic entity attribute of the input document with the semantic entity attribute of the candidate documents in the subset to identify semantic entities having matching semantic attributes, calculating a content similarity score between the semantic entity of the input document and the semantic entity of the candidate document in the subset, calculating a total similarity score for the candidate documents in the subset based on the content similarity score, a number of matching document classification attributes, and weight factors, and selecting similar documents from the subset based on the total similarity score.

The disclosure provides a method for generating an index file of a semantic search engine, and a method for generating a response to a user text query using the index file of the semantic search engine. The disclosure enables semantic searching of text documents or text data items to be performed.

Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for providing information cards using semantic graph data. In some implementations, semantic graph data for a semantic graph is stored, where the semantic graph data indicates objects and relationships among the objects, and the objects include a card object that represents characteristics of an information card. A request is received from a client device, and the request is processed using the semantic graph data. Data for the information card is provided to the client device based on the card object indicated by the semantic graph data.

An apparatus comprises a processing platform implementing a data set discovery engine and a data set valuation engine. The data set discovery engine is configured to generate data set similarity measures each relating a corresponding one of a plurality of data sets to one or more other ones of the plurality of data sets. The data set valuation engine is coupled to the data set discovery engine and configured to generate valuation measures for respective ones of at least a subset of the plurality of data sets based at least in part on respective ones of the data set similarity measures generated by the data set discovery engine. For example, the data set valuation engine may generate the valuation measure for a given data set as a function of valuation measures previously generated for respective other data sets determined to exhibit at least a threshold similarity to the given data set.

In some implementations, systems and methods that are capable of customizing negative media searches using domain-specific search indexes are described. Data indicating a search query associated with a negative media search for an entity and a corpus of documents to be searched are obtained. Content from a particular collection of documents from among the corpus of documents is obtained and processed. Multiple scores for the entity are computed based on processing the content obtained from the collection of documents. The multiple scores are aggregated to compute a priority indicator that represents a likelihood that the collection of documents includes content that is descriptive of derogatory information.

In one embodiment, a method includes receiving an initial input in a first modality from a first user from a client system associated with the first user, determining one or more intents corresponding to the initial input by an intent-understanding module, generating one or more candidate continuation-inputs based on the one or more intents, where the one or more candidate continuation-inputs are in one or more candidate modalities, respectively, and wherein the candidate modalities are different from the first modality, and sending instructions for presenting one or more suggested inputs corresponding to one or more of the candidate continuation-inputs to the client system.

A response system includes an acquisition unit configured to acquire the question sentence of a user, a response sentence selection unit configured to select a response sentence, stored in a storage unit in advance, according to the acquired question sentence, a response sentence conversion unit configured to convert the selected response sentence according to the question sentence, and an output unit configured to output the converted response sentence. The response sentence conversion unit is configured to convert a word included in the selected response sentence to a word included in the question sentence based on the degree of similarity between the word included in the response sentence and the word included in the question sentence.