A retrieval device 10 includes an input unit 11 configured to receive a search query from a user, a retrieval unit 12 configured to calculate a degree of fitness between the search query and each of a plurality of pieces of retrieval target data, a query expansion unit 13 configured to generate an expanded search query, and a policy determination unit 14 configured to determine which of a first process and a second process is to be executed on the basis of the degree of fitness for each piece of the retrieval data calculated by the retrieval unit 12. The first process is presenting the retrieval target data having a high degree of fitness to the user. The second process is proposing to the user that the retrieval unit is caused to calculate the degree of fitness for each piece of the retrieval target data using the expanded search query.

The present invention provides a method for updating and displaying information, which comprises a matrix program that operates as a program to analyze a data list and to form a table separately by setting a plurality of keywords, then produce an alive matrix map after the analysis result is matched and the table is created; therefore, the alive matrix map can add new data lists without repeating the tedious setting steps.

Techniques and systems are described that leverage computer vision as part of search to expand functionality of a computing device available to a user and increase operational computational efficiency as well as efficiency in user interaction. In a first example, user interaction with items of digital content is monitored. Computer vision techniques are used to identify digital images in the digital content, objects within the digital images, and characteristics of those objects. This information is used to assign a user to a user segment of a user population which is then used to control output of subsequent digital content to the user, e.g., recommendations, digital marketing content, and so forth.

A system for and a method of using user-entered information to return more meaningful information in response to Internet search queries are disclosed. A method in accordance with the disclosed subject matter comprises managing a database in response to multiple user inputs and displaying search results from the database in response to a search query. The search results include a results list and supplemental data related to the search query. Managing the database includes, among other things, re-ranking elements in the results list, storing information related to relevancies of elements in the results list, blocking a link in the results list, storing links to documents related to the search query, or any combination of these. The supplemental data include descriptions of or indices to one or more concepts related to the search query.

There is provided a method and a system for generating an interactive search interface in response to a search request by using at least one machine learning (ML) model. A search request such as one of a word, a sentence, a paragraph, and a document is received, and a semantic representation of the search request is received. Semantically similar documents are received by: comparing the search request semantic representation with document representations to obtain semantic similarity scores, and selecting semantically similar documents based on the scores. For each of the set of semantically similar documents, a respective set of similar paragraphs, sentences, and words are determined based on associated representations. An interactive search interface is generated and displayed to a user interface. A selection of a given document is received, and each of the respective set of similar paragraphs, sentences, and similar words associated with the given document are displayed.

A computer implemented method for analyzing feedback with respect to a virtual assistant includes identifying a technical support problem and a corresponding resolution, wherein the technical support problem corresponds to a query, and wherein the corresponding resolution corresponds to the virtual assistant's response, collecting user feedback provided by one or more users corresponding to the technical support problem and the corresponding resolution, creating a set of user profiles corresponding to the one or more users, generating weighted user feedback according to the set of user profiles, identifying contradictory feedback patterns corresponding to the one or more users, adjusting the set of user profiles according to the identified contradictory feedback patterns, and recommending improvements to the identified corresponding resolution.

Systems and methods for summarizing captions, configuring playback speed, and rewriting the caption file for a media asset are disclosed. The system determines whether to display the original captions or a summarized version of the captions, which are based on user's language proficiency level, reading pace, and historical data, and can be generated either on-demand or automatically when rewinds and pauses are detected. The caption file which includes the original captions can be rewritten. The system determines whether to stream a caption or a rewritten file to a media device based on user or system selections. In the absence of a caption file, or when the caption file cannot be summarized, the playback speed of the media asset is slowed down to provide additional reading time to the user.

Embodiments of the present disclosure provide a method, apparatus, device and medium for determining text relevance. The method for determining text relevance may include: identifying, from a predefined knowledge base, a first set of knowledge elements associated with a first text and a second set of knowledge elements associated with a second text. The knowledge base includes a knowledge representation consist of knowledge elements. The method may further include: determining knowledge element relevance between the first set of knowledge elements and the second set of knowledge elements, and determining text relevance between the second text and the first text based at least on the knowledge element relevance.

Systems, computer-implemented methods, and computer program products to facilitate query recommendation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an ontology component that can generate an ontology based on unstructured data of a description of an application programming interface. The computer executable components can further comprise a reasoner component that can identify one or more terms of the ontology that correspond semantically to a term of a query.

A topic-processing system processes topics in a set of documents in a two-stage manner. In the first stage, the system recognizes candidate topics in the set of documents using a machine-trained named-entity recognition (NER) model, to produce original NER information. In a second stage, the system aggregates the original NER information over the set of documents, to produce aggregated information. The system then ranks the candidate topics in the set of candidate topics based on the aggregated information using a machine-trained classification model, to produce a set of ranked topics. The system then selects a set of final topics from the set of ranked topics, e.g., by excluding ranked topics having scores below a prescribed threshold value. A production system presents supplemental information regarding selected final topics, where those final topics are identified by the topic-processing system.