Provided is a learning assistance technology that uses a learning history to check the level of comprehension by a learner in relation to a learning target. Included are a score calculation unit that uses a first occurrence ratio α(n) of a learning target Q(n) calculated using an occurrence frequency R(n) of the learning target Q(n) in a document to be used as a basis for creating a confirmation question and a second occurrence ratio β(n) of the learning target Q(n) weighted by viewing time and calculated using the occurrence frequency R(n) of the learning target Q(n) and a viewing time for each page of the document included in a learning history to calculate one of the difference between the first occurrence ratio α(n) and the second occurrence ratio β(n), the absolute value of the difference, or the ratio as a score S(n) of the learning target Q(n), and a query generation unit that treats the learning target Q(n) corresponding to the n for which the score S(n) is maximized as a query, that is, the learning target with which to create a confirmation question for a learner.

The present disclosure provides systems and methods for building an inventory database with automatic labeling. A system can maintain a hierarchical concept tree including labels. Each of the labels is associated with a set of attributes and a respective embedding. The system can receive, from a provider device, a request to generate labels for an item of media content. The request can include a request attribute. The system can generate, using a gated categorical model, document embeddings for the item of media content. The system can select a subset of the labels based on the request attribute. The system can determine a respective label score for each label of the subset of the labels based on the document embeddings and the respective embedding of the label. The system can provide a selected label of the subset of the labels based on the respective label score of the selected label.

Implementations are directed to determining, based on a submitted query that is a compound query, that a set of multiple sub-queries are collectively an appropriate interpretation of the compound query. Those implementations are further directed to providing, in response to such a determination, a corresponding command for each of the sub-queries of the determined set. Each of the commands is to a corresponding agent (of one or more agents), and causes the agent to generate and provide corresponding responsive content. Those implementations are further directed to causing content to be rendered in response to the submitted query, where the content is based on the corresponding responsive content received in response to the commands.

A system allows users to perform analysis of objects processed by systems, for example, requests, traces, logs, and so on. The system allows users to use an object tagging language to categorize objects. Tagging rules specified using the object tagging language are executed to tag the objects processed. The system created a tagging metadata index based on the tagged objects. The tagging metadata index allows efficient execution of queries used for analyzing the objects. The system may be used for analyzing execution of systems, for example, to compare execution of replicas of a system to determine whether there are differences in the execution of different replicas.

An apparatus and computer implemented method that include obtaining, into a computer, text of a patent, automatically finding and extracting, using the computer, a set of claim text from the patent text, identifying, using the computer, text of independent claims from the set of claim text, displaying in a first row on a computer monitor the text of the independent claims, automatically determining a plurality of preliminary scope-concept phrases from the text of the independent claims, displaying in a second row on the computer monitor the text of the plurality of preliminary scope-concept phrases, eliciting and receiving user input to specify a first one of the plurality of preliminary scope-concepts phrases, and highlighting each occurrence of the specified first one of the plurality of preliminary scope-concept phrases in a plurality of the independent claims displayed in the first row. A scope concept builder tool is also provided.

Techniques and systems are described for performing semantic text searches. A semantic text-searching solution uses a machine learning system (such as a deep learning system) to determine associations between the semantic meanings of words. These associations are not limited by the spelling, syntax, grammar, or even definition of words. Instead, the associations can be based on the context in which characters, words, and/or phrases are used in relation to one another. In response to detecting a request to locate text within an electronic document associated with a keyword, the semantic text-searching solution can return strings within the document that have matching and/or related semantic meanings or contexts, in addition to exact matches (e.g., string matches) within the document. The semantic text-searching solution can then output an indication of the matching strings.

The present application discloses a method and an apparatus for information query, and an electronic device, which relates to a field of deep learning (DL), natural language processing (NLP) and artificial intelligence (AI) technology. The method includes: receiving a query sentence, segmenting the query sentence to obtain word segments, and obtaining a dependency relationship between two word segments and part of speech of the word segments; obtaining a coding sequence of the query sentence according to the dependency relationship and the part of speech of the word segments; matching the coding sequence with a generalized template to obtain a core corpus of the query sentence, wherein the generalized template comprises part of speech to be extracted and a dependency relationship to be extracted; and obtaining a query result corresponding to the query sentence based on the core corpus. The application no longer relies on the accumulation of massive business scenario data to enhance a generalization ability, which ensures accurate and efficient information query, and improves the efficiency and reliability of the information query process. At the same time, it may support information query in different business scenarios, with strong expansion capability and high universality.

A media guidance application may monitor a conversation among users, and identify keywords in the conversation, without the use of wakewords. The keywords are used to search for media content that is relevant to the on-going conversation. Accordingly, the media guidance application presents relevant content to the users, during the conversation, to more actively engage the users. A conversation monitoring window may be used to present conversation information as well as relevant content. A listening mode may be used to manage when the media guidance application processes speech from a conversation. The media guidance application may access user profiles for keywords, select content types, select content sources, and determine relevancy of media content, to provide content in context of a conversation.

The present application provides a robot response method, apparatus, device and storage medium. The method includes: obtaining, by a robot, current query voice; extracting semantic information of the current query voice; matching the semantic information of the current query voice with multiple semantic information clusters stored in advance to get a matched target semantic information cluster, where each semantic information cluster includes: at least one Q&A instance, and each Q&A instance includes: semantic information corresponding to a historical query voice and a query question selected in a query list corresponding to the historical query voice; and obtaining, by the robot, the number of times each query question was selected in the target semantic information cluster, determining, according to the number of times each query question was selected, a target query question corresponding to the current query voice, and outputting a query response corresponding to the target query question.

According to one implementation, a system includes a computing platform having processing hardware, and a system memory storing a software code. The processing hardware is configured to execute the software code to receive a vocabulary, identify words from the vocabulary for use in extending the vocabulary, pair each of those words with every other of those words to provide word pairs, and output the word pairs to a vocabulary administrator. The software code also receives word pair characterizations identifying each of the word pairs as one of similar, dissimilar, or neither similar nor dissimilar, configures, based on the word pair characterizations, a multi-dimensional vector space including multiple embedding vectors each corresponding respectively to one of the identified words, and cross-references each of those words with its corresponding embedding vector to produce an extended vocabulary corresponding to the received vocabulary.