There is provided a method and system for training and using a transformer language model (TLM) part of a recommendation engine. Natural language discussions about a category of items are received, the discussions comprising tags each indicative of a respective item belonging to the category of item. Information is received for each respective item. Based on the natural language discussions, the tags and the information about the respective item, the TLM is trained to: upon receipt of a user input, determine whether a given item should be recommended based on the user input, if the given item should be recommended, retrieving given information about the given item and generating a response to the user input, the response to the user input comprising the given item to be recommended and the given information, and output the response to the user input. The response is generated in natural language format.

Provided is a method for querying questions. The method includes: acquiring input information of a user; acquiring intention information of the user based on the input information of the user; determining an answer generation rule; and generating, based on the input information and the intention information, a first answer in accordance with the answer generation rule, and providing the first answer to the user.

Provided is a method for querying questions. The method includes: acquiring input information of a user; acquiring intention information of the user based on the input information of the user; determining an answer generation rule; and generating, based on the input information and the intention information, a first answer in accordance with the answer generation rule, and providing the first answer to the user.

An intelligent reminding method is provided, which is applicable to a first electronic device, and includes: receiving a message sent by a second electronic device, where the message is a first message received by a first application, and the first message includes a task that needs to be processed by a first user; determining whether there is first interaction information in the first electronic device, where an occurrence time of the first interaction information is later than a time point when the first message is received, and an interaction object of the first interaction information is a second user operating the second electronic device; and presenting reminding information in a case that there is not the first interaction information in the first electronic device, where the reminding information is used for reminding the first user that the task is not completed.

A method for remote damage assessment of a vehicle is provided. The present disclosure relates to the technical field of artificial intelligence, in particular to the technical field of image and text recognition. An implementation solution is: performing data collection on a target vehicle to determine damage information of the target vehicle; obtaining call content of an insurance claiming call for the target vehicle, and extracting accident-related information from the call content, wherein the accident-related information includes named entities in the call content and a relationship between the named entities; and determining a first fraud probability corresponding to the target vehicle at least based on the damage information and the accident-related information.

A method and apparatus for pre-training a model, a device, a storage medium, and a program product. An embodiment of the method includes: acquiring a sample natural language text; generating N types of prompt words based on the sample natural language text, where N is a positive integer; generating sample input data based on the sample natural language text and the N types of prompt words; and training an initial language model based on the sample input data, to obtain a pre-trained language model.

A learning data generation device includes processing circuitry to extract a cause expression and a result expression from an input text, and to generate a modified text by at least one of a method of interchanging the cause expression and the result expression and a method of specifying one of the cause expression and the result expression as a modification target sentence and replacing the modification target sentence with a replacement candidate sentence dissimilar to the modification target sentence.

Provided are a system for recommending related data based on similarity, and a method thereof, the system including: a data collection device; an event extraction device; a data cleansing device; an event vector generation device; an artificial intelligence learning device; and a similar data recommendation device. The present disclosure is directed to providing a system for recommending related data based on similarity and a method thereof, wherein unstructured open data on a webpage is collected to automatically generate an event label for determining a similarity relation, and an artificial intelligence (AI)-based model is trained to group and recommend semantically similar related data, thereby effectively helping users including data scientists who want to see meaningful results through open data.

Provided are a system for recommending related data based on similarity, and a method thereof, the system including: a data collection device; an event extraction device; a data cleansing device; an event vector generation device; an artificial intelligence learning device; and a similar data recommendation device. The present disclosure is directed to providing a system for recommending related data based on similarity and a method thereof, wherein unstructured open data on a webpage is collected to automatically generate an event label for determining a similarity relation, and an artificial intelligence (AI)-based model is trained to group and recommend semantically similar related data, thereby effectively helping users including data scientists who want to see meaningful results through open data.

A computer-implemented method, system and computer program product for improving understandability of text by a user. A final word vector for each word in a sentence of a document is computed, such as by averaging a first word vector and a second word vector for that word. Furthermore, elements of a user portrait are vectorized. A distance is then computed between a vector for each word in the sentence and a vectorized element in the user’s portrait which is summed to form an evaluation result for the element. An evaluation result is also formed for every other element in the user’s portrait by performing such a computation step. A “final evaluation result” is then generated corresponding to the evaluation results for every element in the user’s portrait. The document is then transformed in response to the final evaluation result indicating a lack of understanding of the sentence by the user.