Abstract: A method of training a model, a method of determining a word vector, a device, a medium, and a product are provided, which may be applied to fields of natural language processing, information processing, etc. The method includes: acquiring a first word vector set corresponding to a first word set; and generating a reduced-dimensional word vector for each word vector in the first word vector set based on a word embedding model, generating, for other word vector in the first word vector set, a first probability distribution in the first word vector set based on the reduced-dimensional word vector, and adjusting a parameter of the word embedding model so as to minimize a difference between the first probability distribution and a second probability distribution for the other word vector determined by a number of word vector in the first word vector set.

Abstract: The present disclosure provides a method and an apparatus of matching data, a device and a computer-readable storage medium, which are related to the field of artificial intelligence technology, and in particularly to the field of intelligent search and deep learning. The specific implementation solution includes: obtaining a first instance of a resume and a second instance of a job profile; determining, for a meta path, a resume feature data of the first instance and a profile feature data of the second instance, the meta path is a knowledge graph path from the resume to the job profile; and applying a classification model to the resume feature data of the first instance and the profile feature data of the second instance to determine a matching result between the first instance and the second instance.

Abstract: A method, an apparatus, a device and a medium for generating a recruitment position description text are provided. The method may include: obtaining the original text related to the target position; and generating a target recruitment position description text corresponding to the target position based on the original text and a pre-trained deep neural network model.

Abstract: A method for community search is performed by an electronic device. The method includes: obtaining graph data to be processed, in which the graph data includes a plurality of nodes and a plurality of connection edges between the nodes; determining a query node from the plurality of nodes based on the graph data, and determining a target community to which the query node belongs by performing a community search for the query node, in which the target community includes the query node, and at least one node other than the query node in the plurality of nodes; and determining the query node and performing the community search for the query node repeatedly until the community to which each node included in the graph data belongs is determined.

Abstract: A method, an apparatus, a device, a storage medium and a program product of performing a text matching are provided, which relate to a field of a computer technology, and in particular to natural language processing and deep learning technologies. The method includes: determining a word set and a plurality of semantic units from a text set, the word set is associated with a first predetermined attribute, and the text set contains a plurality of first texts indicating an object information and a plurality of second texts indicating an object demand information; generating a graph; and generating a final feature representation associated with the text set and the word set based on the graph and a graph convolution model, so as to perform the text matching.

Abstract: This application discloses a keyword generating method, an apparatus, a device and a storage medium, which relate to the field of natural language processing in the field of artificial intelligence. A specific implementation scheme includes: inputting a target text into a text processing model, obtaining a word sequence corresponding to the target text, and generating a semantic representation sequence corresponding to the word sequence; making prediction about each semantic representation vector in the semantic representation sequence respectively to obtain a prediction result; and if the prediction result indicates that a word corresponding to the semantic representation vector is capable of triggering a generation of a keyword, outputting the keyword based on the semantic representation vector and the prediction result. This method improves the accuracy of generating keywords.

Abstract: A method and an apparatus for extracting a skill label, and a method and an apparatus for training a candidate phrase classification model are provided. The method for extracting the skill label includes obtaining a plurality of words by performing word segmentation on a sentence to be extracted, and determining a multi-dimensional feature vector of each word; extracting a candidate phrase from the sentence to be extracted; determining a multi-dimensional feature vector of each word in the candidate phrase according to the multi-dimensional feature vector of each word; generating a semantic representation vector of the candidate phrase according to the multi-dimensional feature vector of each word in the candidate phrase; and extracting the skill label from the sentence to be extracted based on the semantic representation vector of the candidate phrase.

Abstract: A method for community search is performed by an electronic device. The method includes: obtaining graph data to be processed, in which the graph data includes a plurality of nodes and a plurality of connection edges between the nodes; determining a query node from the plurality of nodes based on the graph data, and determining a target community to which the query node belongs by performing a community search for the query node, in which the target community includes the query node, and at least one node other than the query node in the plurality of nodes; and determining the query node and performing the community search for the query node repeatedly until the community to which each node included in the graph data belongs is determined.

Abstract: A method and an apparatus for constructing an object relationship network and an electronic device are provided by the present disclosure, relating to the field of artificial intelligence technologies, such as deep neural networks, deep learning, etc. A specific implementation solution is: extracting keywords in respective text contents corresponding to a plurality of objects to obtain keywords corresponding to respective objects; and according to the keywords corresponding to the objects, a similarity between the plurality of objects is determined; and then according to the similarity between the plurality of objects, an object relationship network between the plurality of objects is constructed. Since the object relationship network constructed by means of the similarity between the plurality of objects can accurately describe a closeness degree of a relationship between the objects, thus, the plurality of objects can be managed effectively by means of the constructed object relationship network.

Abstract: The present disclosure provides a method and an apparatus of matching data, a device and a computer-readable storage medium, which are related to the field of artificial intelligence technology, and in particularly to the field of intelligent search and deep learning. The specific implementation solution includes: obtaining a first instance of a resume and a second instance of a job profile; determining, for a meta path, a resume feature data of the first instance and a profile feature data of the second instance, the meta path is a knowledge graph path from the resume to the job profile; and applying a classification model to the resume feature data of the first instance and the profile feature data of the second instance to determine a matching result between the first instance and the second instance.

Abstract: The present disclosure provides a method of processing data, a device and a computer-readable storage medium, which relates to a technical field of artificial intelligence, and in particular to fields of intelligent search and deep learning. The method includes: generating a resume heterogeneous graph and a job heterogeneous graph; determining a first matching feature representation for the resume and the job profile based on first and second node feature representations for a first node in the resume heterogeneous graph and a second node in the job heterogeneous graph respectively; determining a second matching feature representation for the resume and the job profile based on first and second graph feature representations for the resume heterogeneous graph and the job heterogeneous graph respectively; and determining a similarity between the resume and the job profile based on the first and second matching feature representations.

Abstract: A method of training a model, a method of determining a word vector, a device, a medium, and a product are provided, which may be applied to fields of natural language processing, information processing, etc. The method includes: acquiring a first word vector set corresponding to a first word set; and generating a reduced-dimensional word vector for each word vector in the first word vector set based on a word embedding model, generating, for other word vector in the first word vector set, a first probability distribution in the first word vector set based on the reduced-dimensional word vector, and adjusting a parameter of the word embedding model so as to minimize a difference between the first probability distribution and a second probability distribution for the other word vector determined by a number of word vector in the first word vector set.

Abstract: A method, an apparatus, a device, a storage medium and a program product of performing a text matching are provided, which relate to a field of a computer technology, and in particular to natural language processing and deep learning technologies. The method includes: determining a word set and a plurality of semantic units from a text set, the word set is associated with a first predetermined attribute, and the text set contains a plurality of first texts indicating an object information and a plurality of second texts indicating an object demand information; generating a graph; and generating a final feature representation associated with the text set and the word set based on the graph and a graph convolution model, so as to perform the text matching.

Abstract: The present disclosure provides a skill word evaluation method for a resume, and relates to the technical field of machine learning. The method includes determining a to-be-evaluated first skill word list including a plurality of skill words, according to a resume document to be evaluated; and predicting, for each skill word in the first skill word list, a value of probability of presence of the skill word for representing importance of the skill word, by a pre-trained skill word evaluation model according to context information of the skill word in the first skill word list. The present disclosure further provides a skill word evaluation device, an electronic device and a non-transitory computer readable storage medium.

Abstract: This application discloses a keyword generating method, an apparatus, a device and a storage medium, which relate to the field of natural language processing in the field of artificial intelligence. A specific implementation scheme includes: inputting a target text into a text processing model, obtaining a word sequence corresponding to the target text, and generating a semantic representation sequence corresponding to the word sequence; making prediction about each semantic representation vector in the semantic representation sequence respectively to obtain a prediction result; and if the prediction result indicates that a word corresponding to the semantic representation vector is capable of triggering a generation of a keyword, outputting the keyword based on the semantic representation vector and the prediction result. This method improves the accuracy of generating keywords.

Abstract: A method, an apparatus, a device and a medium for generating a recruitment position description text are provided. The method may include: obtaining the original text related to the target position; and generating a target recruitment position description text corresponding to the target position based on the original text and a pre-trained deep neural network model.