Abstract: An automatic righting system includes a discrimination calibration module, a righting module, and a controller. The discrimination calibration module includes a distance sensing unit. The distance sensing unit is located at the front end of the glasses and configured to acquire the distance between the distance sensing unit and a first feature position. The righting module includes a push-up air cushion. The push-up air cushion is mounted at the front end of the glasses. The push-up air cushion is configured to be inflated to push the glasses to move upward. The controller is electrically connected to the distance sensing unit and the push-up air cushion. The controller is configured to control the push-up air cushion to start or stop to right the glasses according to a distance signal of the distance sensing unit.

Abstract: A semantic classification model training method includes that a sample query template and a label category of at least one category to be predicted in the sample query template are acquired, where the sample query template is constructed according to a sample query statement and a number of the at least one category to be predicted; the sample query template is input to the pre-constructed semantic classification model to obtain a sample semantic category of the at least one category to be predicted; and the semantic classification model is trained according to the sample semantic category and the label category of the at least one category to be predicted.

Abstract: A question answering method, a method of training a question answering model, a device, and a medium are provided, which relate to a field of artificial intelligence technology, in particular to fields of natural language processing technology, deep learning technology, and knowledge mapping technology. The question answering method includes: obtaining data to be processed, wherein the data to be processed includes a question and candidate answers; performing general semantic understanding on the data to be processed to obtain a general data feature; selecting a target question answering mode from candidate question answering modes based on the general data feature; and processing the general data feature by using the target question answering mode, to obtain a target answer for the question from the candidate answers.

Abstract: An artificial intelligence based method and apparatus for processing information. A specific embodiment of the method includes: acquiring a search record set within a preset time period; matching the search record set with an encyclopedia entry set, and selecting a first set of points of interest from the encyclopedia entry set according to a match result; matching the search record set with a microblog topic set, and selecting a second set of points of interest from the microblog topic set according to a match result; and adding the first set of points of interest and the second set of points of interest to a set of points of interest. This embodiment achieves an accurate acquisition of the points of interest, thus facilitating the implementation of a targeted information push.

Abstract: A method of processing data, a device, and a medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision, natural language technology, speech technology, deep learning and knowledge graph. The method of processing data includes: generating a video feature, a question feature and an answer feature based on acquired video data, acquired question data and acquired candidate answer data; determining a link relationship between the video feature, the question feature and the answer feature; and determining a matching result for the video data, the question data and the candidate answer data based on the link relationship.

Abstract: An artificial intelligence based method and apparatus for processing information. A specific embodiment of the method includes: acquiring search click information recorded within a predetermined time period; generating a candidate entry set by selecting, from the search click information, entries having click volumes exceeding a click volume threshold within a preset unit time period; forming, for each candidate entry in the candidate entry set, a click volume sequence according to a chronological order of each of the click volumes corresponding to the candidate entry in the predetermined time period; determining, based on click volume sequences, categories of the candidate entries respectively corresponding to click volume sequences; and determining candidate entries having the categories being a preset category as points of interest to generate a set of points of interest.

Abstract: The present disclosure disclosures an artificial intelligence based method and apparatus for determining regional information. A specific embodiment of the method comprises: acquiring to-be-determined information, and extracting a keyword set of the to-be-determined information; inputting the keyword set of the to-be-determined information into a pre-trained subject classification model for classifying, to obtain a subject category of the to-be-determined information, wherein the subject classification model is used for representing a corresponding relation between the keyword set of the information and the subject category of the information; selecting, from a pre-stored place name set, a place name corresponding to the subject category of the to-be-determined information as a target place name set; matching, in the to-be-determined information, the target place name set; and determining, based on a matching result, whether the to-be-determined information belongs to the regional information.

Abstract: The present application discloses a text processing method and device based on natural language processing and a knowledge graph, and relates to the in-depth field of artificial intelligence technology. A specific implementation is: an electronic device uses a joint learning model to obtain a semantic representation, which is obtained by the joint learning model by combining knowledge graph representation learning and natural language representation learning, it combines a knowledge graph representation learning and a natural language representation learning, compared to using only the knowledge graph representation learning or the natural language representation learning to learn semantic representation of a prediction object, factors considered by the joint learning model are more in quantity and comprehensiveness, so accuracy of semantic representation can be improved, and thus accuracy of text processing can be improved.

Abstract: A method and apparatus for pushing information. A specific implementation of the method includes: parsing page content browsed by a user to extract a keyword of the page content and determine a theme corresponding to the keyword; determining a preset keyword matching the keyword in a preset keyword collection; determining an associated keyword associated with the determined preset keyword based on a pre-established associated relationship of the preset keyword; and pushing information corresponding to the theme and/or the associated keyword to the user. This implementation implements targeted information push.

Abstract: The present disclosure discloses an artificial intelligence based method and apparatus for processing information. A specific embodiment of the method includes: acquiring a search record set within a preset time period; matching the search record set with an encyclopedia entry set, and selecting a first set of points of interest from the encyclopedia entry set according to a match result; matching the search record set with a microblog topic set, and selecting a second set of points of interest from the microblog topic set according to a match result; and adding the first set of points of interest and the second set of points of interest to a set of points of interest. This embodiment achieves an accurate acquisition of the points of interest, thus facilitating the implementation of a targeted information push.

Abstract: A method and apparatus for pushing information. A specific implementation of the method includes: parsing page content browsed by a user to extract a keyword of the page content and determine a theme corresponding to the keyword; determining a preset keyword matching the keyword in a preset keyword collection; determining an associated keyword associated with the determined preset keyword based on a pre-established associated relationship of the preset keyword; and pushing information corresponding to the theme and/or the associated keyword to the user. This implementation implements targeted information push.

Abstract: The present disclosure discloses an artificial intelligence based method and apparatus for processing information. A specific embodiment of the method includes: acquiring search click information recorded within a predetermined time period; generating a candidate entry set by selecting, from the search click information, entries having click volumes exceeding a click volume threshold within a preset unit time period; forming, for each candidate entry in the candidate entry set, a click volume sequence according to a chronological order of each of the click volumes corresponding to the candidate entry in the predetermined time period; determining, based on click volume sequences, categories of the candidate entries respectively corresponding to click volume sequences; and determining candidate entries having the categories being a preset category as points of interest to generate a set of points of interest.

Abstract: The present disclosure discloses an artificial intelligence based method and apparatus for determining regional information. A specific embodiment of the method comprises: acquiring to-be-determined information, and extracting a keyword set of the to-be-determined information; inputting the keyword set of the to-be-determined information into a pre-trained topic classification model for classifying, to obtain a topic class of the to-be-determined information, wherein the topic classification model is used for representing a corresponding relation between the keyword set of the information and the topic class of the information; selecting, from a pre-stored place name set, a place name corresponding to the topic class of the to-be-determined information as a target place name set; matching, in the to-be-determined information, the target place name set; and determining, based on a matching result, whether the to-be-determined information belongs to the regional information.

Abstract: The present disclosure discloses an artificial intelligence based method and apparatus for generating an article. A specific embodiment of the method comprises: acquiring predetermined structure data for generating an article; generating candidate sentences from the predetermined structure data using a sentence generation model; forming a chapter by splicing candidate sentences selected according to a probability for a sentence containing a preset information point appearing, wherein each time a candidate sentence is selected, candidate sentences relating to the selected candidate sentence are excluded according to a preset exclusion rule; and generating an article based on the chapter formed by splicing, in response to no candidate sentence being available.

Abstract: Disclosure relates to a statistical machine translation field, and more particularly to a method and a device for filtering a translation rule and generating a target word in a hierarchical phrase-based statistical machine translation. The method and device filters a translation rule using a relaxed-well-formed dependency structure and generates a target word by referring to a head word of a source word in a hierarchical phrase-based statistical machine translation. The disclosure improves a translation performance while reducing a number of translation rules, in comparison with a hierarchical phrase-based original translation rule table.

Abstract: Candidate suggestions for correcting misspelled query terms input into a search application are automatically generated. A score for each candidate suggestion can be generated using a first decoding pass and paths through the suggestions can be ranked in a second decoding pass. Candidate suggestions can be generated based on typographical errors, phonetic mistakes and/or compounding mistakes. Furthermore, a ranking model can be developed to rank candidate suggestions to be presented to a user.

Abstract: Candidate suggestions for correcting misspelled query terms input into a search application are automatically generated. A score for each candidate suggestion can be generated using a first decoding pass and paths through the suggestions can be ranked in a second decoding pass. Candidate suggestions can be generated based on typographical errors, phonetic mistakes and/or compounding mistakes. Furthermore, a ranking model can be developed to rank candidate suggestions to be presented to a user.

Abstract: Identifying and selecting keywords in a second language based on an input keyword from a user in a first language. Translation candidates in the second language are determined from the input keyword. Keywords in the second language related to the translation candidates are identified and included with the translation candidates. The translation candidates are ranked and presented to the user for selection.