The present disclosure provides a word mining method and apparatus, an electronic device and a readable storage medium, and relates to the field of artificial intelligence technologies, such as natural language processing technologies, deep learning technologies, cloud service technologies, or the like. The word mining method includes: acquiring search data; taking first identification information, a search sentence and second identification information in the search data as nodes, and taking a relationship between the first identification information and the search sentence, a relationship between the first identification information and the second identification information and a relationship between the search sentence and the second identification information as sides to construct a behavior graph; obtaining a label vector of each search sentence in the behavior graph according to a search sentence with a preset label in the behavior graph; determining a target search sentence in the behavior graph according to the label vector; and extracting a target word from the target search sentence, and taking the target word as a word mining result of the search data.

The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

Systems and methods for automated and explainable machine learning to generate seamlessly actionable insights by generating explainable personas directly from customer relationship management systems are disclosed. The personas are defined as a collection of segments, scored by likelihood to generate good opportunities, accompanied ranked profile attribute importance, with descriptive names and summaries, associated human and database readable queries which have been generated to optimally find cluster candidates in a broader data universe. Such a system would effectively and accurately model the composition of past clients, perform the categorization in an explainable way such that actions can be taken on the information to have predictable results. What is further required are the mean to categorize small text components, trained over dependent and independent model sets, to enable a cleaner and more explicit representation of information rich short-strings, in order to facilitate a more meaningful representation of the user profiles.

Examples provide a self-supervised language model for document-to-document similarity scoring and ranking long documents of arbitrary length in an absence of similarity labels. In a first stage of a two-staged hierarchical scoring, a sentence similarity matrix is created for each paragraph in the candidate document. A sentence similarity score is calculated based on the sentence similarity matrix. In the second stage, a paragraph similarity matrix is constructed based on aggregated sentence similarity scores associated with the first candidate document. A total similarity score for the document is calculated based on the normalize the paragraph similarity matrix for each candidate document in a collection of documents. The model is trained using a masked language model and intra-and-inter document sampling. The documents are ranked based on the similarity scores for the documents.

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.

The invention relates to an interactive system (1) with a user (U) occupying a motor vehicle (100) comprising an interface (3) and an operating system for executing an application. According to the invention, the interactive system (1) comprises a measuring device (5) comprising at least one sensor that is configured to acquire at least one physiological parameter of the user (U), and an embedded processing unit (7). The processing unit (7) comprises an interpretation module that is configured to receive the physiological parameter and to define, on the basis of the physiological parameter, a datum representative of the physiological or emotional state of the user (U), and an analysis module that is configured to compare data representative of the physiological or emotional state of the user (U) before and during the execution of an application for managing a remediation exercise, and to adjust at least one parameter of the remediation exercise according to the results of the comparison. The invention also relates to an interaction method implemented at least partly by such an interactive system.

The disclosed apparatus, systems, and methods relate to a location query mechanism that can efficiently determine whether a target entity is located within a region of interest (ROI). At a high level, the location query mechanism can be configured to represent a ROI using one or more polygons. The location query mechanism can, in turn, divide (e.g., tessellate) the one or more polygons into sub-polygons. Subsequently, the location query mechanism can use the sub-polygons to build an index system that can efficiently determine whether a particular location is within any of the sub-polygons. Therefore, when a computing device queries whether a particular location is within the region of interest, the location query mechanism can use the index system to determine whether the particular location is within any of the sub-polygons.

Textual information related to user information from user service information is identified. A layered matching is performed on the textual information based on preset background identification information in a preset list, wherein the layered matching includes different matching methods, and the preset list includes a plurality of entries storing different preset background identification information related to the user information. The user information is determined based on the layered matching.

The present application discloses a method and apparatus for acquiring point-of-interest (POI) state information, a device and a computer storage medium, and relates to the field of big data. An implementation includes acquiring a text containing POI information in a preset time period from the Internet; and identifying the text using a pre-trained POI-state identifying model to obtain a binary group in the text, the binary group including a POI name and the POI state information corresponding to the POI name; wherein the POI-state identifying model performs label prediction of the POI name and a POI state on a word sequence corresponding to the text, and label prediction results of the POI name and the POI state are aligned to obtain the binary group. With the present application, a human cost may be saved, and timeliness and accuracy may be improved.

A terminal is disclosed. The terminal according to an embodiment of the present invention comprises: an output unit for outputting a notification; a storage unit for storing a database; a control unit for controlling the outputting of the notification; and an artificial intelligence unit for acquiring information regarding a user's context, and outputting a notification when the user's context corresponds to information included in the database, wherein the database includes at least one of a user's personal database, a standard activity database, and an accident type database.