Abstract: A computer-implemented method is provided. The method includes: obtaining a sample text and a sample image corresponding to the sample text; labeling a true semantic tag for the sample text according to a first preset rule; obtaining a text feature representation of the sample text and a predicted semantic tag output by a text coding sub-model; obtaining an image feature representation of the sample image output by an image coding sub-model; calculating a first loss based on the true semantic tag and the predicted semantic tag; calculating a contrast loss based on the text feature representation of the sample text and the image feature representation of the sample image; adjusting parameters of the text coding sub-model based on the first loss and the contrast loss; and adjusting parameters of the image coding sub-model based on the contrast loss.

Abstract: Embodiments of the present disclosure provide a method, apparatus, device and medium for determining text relevance. The method for determining text relevance may include: identifying, from a predefined knowledge base, a first set of knowledge elements associated with a first text and a second set of knowledge elements associated with a second text. The knowledge base includes a knowledge representation consist of knowledge elements. The method may further include: determining knowledge element relevance between the first set of knowledge elements and the second set of knowledge elements, and determining text relevance between the second text and the first text based at least on the knowledge element relevance.

Abstract: A method for generating a pre-trained language model, includes: obtaining sample files; obtaining typography structure information and text information of the sample files by parsing the sample files; obtaining a plurality of task models of a pre-trained language model; obtaining a trained pre-trained language model by jointly training the pre-trained language model and the plurality of task models according to the typography structure information and the text information; and generating a target pre-trained language model by fine-tuning the trained pre-trained language model according to the typography structure information and the text information.

Abstract: The disclosure provides a method for processing a video, an electronic device, and a computer storage medium. The method includes: determining a plurality of first identifiers related to a first object based on a plurality of frames including the first object in a target video; determining a plurality of attribute values associated with the plurality of first identifiers based on a knowledge base related to the first object; determining a set of frames from the plurality of frames, in which one or more attribute values associated with one or more first identifiers determined from each one of the set of frames are predetermined values; and splitting the target video into a plurality of video clips based on positions of the set of frames in the plurality of frames.

Abstract: Embodiments of the present provide a method and a device for processing an image, server and storage medium. The method includes: determining, based on an object type of an object in an image to be processed, a feature expression of the object in the image to be processed; and determining an entity associated with the object in the image to be processed based on the feature expression of the object in the image to be processed and a feature expression of an entity in a knowledge graph.

Abstract: The present disclosure provides a text processing method and device based on ambiguous entity words. The method includes: obtaining a context of a text to be disambiguated and at least two candidate entities represented by the text to be disambiguated; generating a semantic vector of the context based on a trained word vector model; generating a first entity vector of each of the at least two candidate entities based on a trained unsupervised neural network model; determining a similarity between the context and each candidate entity; and determining a target entity represented by the text to be disambiguated in the context.

Abstract: The present disclosure discloses a video classification method, an electronic device and a storage medium, and relates to the field of computer technologies, and particularly to the field of artificial intelligence technologies, such as knowledge graph technologies, computer vision technologies, deep learning technologies, or the like. The video classification method includes: extracting a keyword in a video according to multi-modal information of the video; acquiring background knowledge corresponding to the keyword, and determining a text to be recognized according to the keyword and the background knowledge; and classifying the text to be recognized to obtain a class of the video.

Abstract: The present disclosure discloses a method for training a cross-modal retrieval model, an electronic device and a storage medium, and relates to the field of computer technologies, and particularly to the field of artificial intelligence technologies, such as knowledge graph technologies, computer vision technologies, deep learning technologies, or the like. The method for training a cross-modal retrieval model includes: determining similarity of a cross-modal sample pair according to the cross-modal sample pair, the cross-modal sample pair including a sample of a first modal and a sample of a second modal, and the first modal being different from the second modal; determining a soft margin based on the similarity, and determining a soft margin loss function based on the soft margin; and determining a total loss function based on the soft margin loss function, and training a cross-modal retrieval model according to the total loss function.

Abstract: Technical solutions for video event recognition relate to the fields of knowledge graphs, deep learning and computer vision. A video event graph is constructed, and each event in the video event graph includes: M argument roles of the event and respective arguments of the argument roles, with M being a positive integer greater than one. For a to-be-recognized video, respective arguments of the M argument roles of a to-be-recognized event corresponding to the video are acquired. According to the arguments acquired, an event is selected from the video event graph as a recognized event corresponding to the video.

Abstract: A video processing method, an electronic device and a storage medium are provided, and relate to the field of artificial intelligence, and particularly relates to the fields of deep learning, model training, knowledge mapping, video processing and the like. The method includes: acquiring a plurality of first video frames, and performing fine-grained splitting on the plurality of first video frames to obtain a plurality of second video frames; performing feature encoding on the plurality of second video frames according to multi-mode information related to the plurality of second video frames, to obtain feature fusion information for characterizing fusion of the multi-mode information; and performing similarity matching on the plurality of second video frames according to the feature fusion information, and obtaining a target video according to a result of the similarity matching.

Abstract: A method and an apparatus for outputting information are provided according to embodiments of the disclosure. The method includes: recognizing a target video, to recognize at least one entity and obtain a confidence degree of each entity, the entity including a main entity and related entities; matching the at least one entity with a pre-stored knowledge base to determine at least one candidate entity; obtaining at least one main entity by expanding the related entities of the at least one candidate entity based on the knowledge base, and obtaining a confidence degree of the obtained main entity; and calculating a confidence level of the obtained main entity based on the confidence degree of each of the related entities of the at least one candidate entity and the confidence degree of the obtained main entity, and outputting the confidence level of the obtained main entity.

Abstract: A method, apparatus, device, and storage medium for linking an entity, relates to the technical fields of knowledge graph and deep learning are provided. The method may include: acquiring a target text; determining at least one entity mention included in the target text and a candidate entity corresponding to each entity mention; determining an embedding vector of each candidate entity based on the each candidate entity and a preset entity embedding vector determination model; determining context semantic information of the target text based on the target text and each embedding vector; determining type information of the at least one entity mention; and determining an entity linking result of the at least one entity mention, based on the each embedding vector, the context semantic information, and each type information.

Abstract: The present disclosure provides a method, a device, an equipment and a storage medium for mining a topic concept. The method includes: acquiring a plurality of candidate topic concepts based on a query; performing word segmentation on the plurality of candidate topic concepts and performing part-of-speech tagging on words obtained after performing the word segmentation, to obtain a part-of-speech sequence of each of the plurality of candidate topic concepts; and filtering the plurality of candidate topic concepts based on the part-of-speech sequence, to filter out a topic concept corresponding to a target part-of-speech sequence among the plurality of candidate topic concepts, in which a proportion of accurate topic concepts in the target part-of-speech sequence is lower than or equal to a first preset threshold, or a proportion of inaccurate topic concepts in the target part-of-speech sequence is higher than or equal to a second preset threshold.

Abstract: A theme classification method based on multimodality is related to a field of a knowledge map. The method includes obtaining text information and non-text information of an object to be classified. The non-text information includes at least one of visual information and audio information. The method also includes determining an entity set of the text information based on a pre-established knowledge base, and then extracting a text feature of the object based on the text information and the entity set. The method also includes determining a theme classification of the object based on the text feature and a non-text feature of the object.

Abstract: The disclosure provides a method for processing a video, an electronic device, and a computer storage medium. The method includes: determining a plurality of first identifiers related to a first object based on a plurality of frames including the first object in a target video; determining a plurality of attribute values associated with the plurality of first identifiers based on a knowledge base related to the first object; determining a set of frames from the plurality of frames, in which one or more attribute values associated with one or more first identifiers determined from each one of the set of frames are predetermined values; and splitting the target video into a plurality of video clips based on positions of the set of frames in the plurality of frames.

Abstract: A method, electronic device and storage medium for generating information are disclosed. The method includes: acquiring a plurality of tag entity words from a target video, the tag entity words including a person entity word, a work entity word, a video category entity word, and a video core entity word, the video core entity word including an entity word for characterizing a content related to the target video; linking, for a tag entity word among the plurality of tag entity words, the tag entity word to a node of a preset knowledge graph; determining semantic information of the target video based on a linking result of each of the tag entity words; and structuring the semantic information of the target video based on a relationship between the node and an edge of the knowledge graph, to obtain structured semantic information of the target video.

Abstract: A method, apparatus, device, and storage medium for entity linking is disclosed. The method includes: acquiring a target text; determining at least one entity mention included in the target text; determining a candidate entity corresponding to each of the entity mention based on a preset knowledge base; determining a reference text of each of the candidate entity and determining additional feature information of each of the candidate entity; and determining an entity linking result based on the target text, each of the reference text, and each piece of the additional feature information.

Abstract: A method and an apparatus for labelling a core entity, and a related electronic device are proposed. A character vector sequence, a first word vector sequence and an entity vector sequence corresponding to a target text are obtained by performing character vector mapping, word vector mapping and entity vector mapping are performed on the target text, to obtain a target vector sequence corresponding to the target text. A first probability that each character of the target text is a starting character of a core entity and a second probability that each character of the target text is an ending character of a core entity are determined by encoding and decoding the target vector sequence. One or more core entities of the target text are determined based on the first probability and the second probability.

Abstract: A method and an apparatus for generating a text topic and an electronic device are disclosed. The method includes: obtaining entities included in a text to be processed by mining the entities; determining each candidate entity in a knowledge graph corresponding to each entity included in the text to be processed through entity links; determining a set of core entities corresponding to the text to be processed by clustering candidate entities; determining each candidate topic included in the text to be processed based on a matching degree between each keyword in the text to be processed and each reference topic in a preset topic graph; and obtaining the text topic from the set of core entities and the candidate topics based on association between each core entity and the text to be processed as well as association between each candidate topic and the text to be processed.

Abstract: A method for mining an entity focus in a text may include: performing word and phrase feature extraction on an input text; inputting an extracted word and phrase feature into a text coding network for coding, to obtain a coding sequence of the input text; processing the coding sequence of the input text using a core entity labeling network to predict a position of a core entity in the input text; extracting a subsequence corresponding to the core entity in the input text from the coding sequence of the input text, based on the position of the core entity in the input text; and predicting a position of a focus corresponding to the core entity in the input text using a focus labeling network, based on the coding sequence of the input text and the subsequence corresponding to the core entity in the input text.