Abstract: A method and apparatus for processing a table, a device, a storage medium and a product. An implementation of the method comprise: receiving a content query request for a target table; acquiring a target tree structure of the target table according to the content query request; where, the target tree structure is obtained by performing absorbing processing and merging processing on at least one target cell in the target table; acquiring to-be-queried content in the content query request; and querying target content matching the to-be-queried content from the target tree structure.

Abstract: A magnetic button with a attraction effect is disclosed. The magnetic button includes an adsorption sub-buckle and an adsorption female buckle. Installation pads are arranged at one end of the adsorption sub-buckle and at one end of the adsorption female buckle. A connection clamping column is arranged on one side of the adsorption sub-buckle. Double locking is performed via clamping between the connection clamping column and a female buckle and adsorption between a common magnet and a strong neodymium iron boron magnet. A male buckle concave clamping groove is formed in the connection clamping column. A clamping ring is arranged on an inner wall of the male buckle concave clamping groove.

Abstract: A method and an apparatus for processing information are provided. The method can include: acquiring a word sequence obtained by performing word segmentation on two paragraphs in a text; inputting the word sequence into a to-be-trained natural language processing model to generate a word vector corresponding to a word in the word sequence; inputting the word vector into a preset processing layer of the to-be-trained natural language processing model; predicting whether the two paragraphs are adjacent, and a replaced word in the two paragraphs; and acquiring reference information of the two paragraphs, and training the to-be-trained natural language processing model to obtain a trained natural language processing model, based on the prediction result and the reference information.

Abstract: Provided by the present disclosure is a new category tag mining method, involving the field of knowledge graph technology, and including: obtaining a plurality of queries during a current preset time period; labeling a category tag on each query of the plurality of queries, by using a pre-trained sequence labeling model, to extract the category tag currently corresponding to the query from the query; and removing a category tag already existing in a preset current category tag library from category tags currently corresponding to all the queries, and determining a remaining category tag as a new category tag. The present disclosure also provides an electronic device and a non-transitory computer-readable storage medium.

Abstract: Disclosed are an information extraction method, an electronic device and a readable storage medium, which relate to the field of artificial intelligence technologies, and particularly to the field of knowledge graph technologies. The information extraction method includes: acquiring to-be-processed text to obtain a semantic vector of each token in the to-be-processed text; generating a relationship prediction matrix, an entity prediction matrix and an alignment matrix according to each token in the to-be-processed text and the semantic vector of each token; and extracting a target triplet in the to-be-processed text using the relationship prediction matrix, the entity prediction matrix and the alignment matrix, and taking the target triplet as an information extraction result of the to-be-processed text.

Abstract: The present disclosure relates to the field of medical data processing based on natural language processing. Embodiments of the present disclosure disclose a method and apparatus for verifying a medical fact. The method may include: acquiring a description text of the medical fact; selecting a relevant paragraph related to the description text of the medical fact from a medical document; and inputting the description text of the medical fact and the corresponding relevant paragraph into a trained discrimination model for authenticity judgment, to obtain a verification result of the medical fact, the discrimination model being pre-trained based on a medical text paragraph pair extracted from the medical document, and being iteratively adjusted using a medical fact sample set including authenticity labeling information after the pre-training.

Abstract: A computer-implemented method for sample augmentation includes: acquiring a second sample corpus and second triplet information of the second sample corpus by performing data augmentation on a first sample corpus labeled with first triplet information; acquiring third triplet information of a third sample corpus by performing semi-supervised learning on the third sample corpus that is not labeled with triplet information; and generating a set of training corpora for a triplet information extraction network based on the first sample corpus and the first triplet information, the second sample corpus and the second triplet information, and the third sample corpus and the third triplet information.

Abstract: A method of extracting a table information, an electronic device, and a storage medium are provided, which relate to fields of artificial intelligence and big data, in particular to fields of machine learning, knowledge graph, intelligent search and intelligent recommendation, and may be used for an intelligent extraction of an information in a table and other scenarios. The method includes: performing a clustering based on features of a plurality of rows of cells and/or features of a plurality of columns of cells in a table, so as to determine candidate header cells in the table; and performing an information extraction on the table based on the candidate header cells, so as to extract attribute-attribute value pairs in the table.

Abstract: The present disclosure provides a method of processing triple data, a method of training a triple data processing model, an electronic device, and a storage medium. A specific implementation solution includes: performing a triple data extraction on text data to obtain a plurality of field data; normalizing the plurality of field data to determine target triple data, wherein the target triple data contains entity data, entity relationship data, and association entity data; and verifying a confidence level of the target triple data to obtain a verification result.

Abstract: Embodiments of the present disclosure provide to a method and a device for acquiring a data model in a knowledge graph, an apparatus and a storage medium. The method includes: receiving a knowledge entry describing a relationship between an entity and an object; determining a plurality of candidate object types of the object according to at least one of the entity, the relationship and the object; determining an object type for generating a data model that matches the knowledge entry from the plurality of candidate object types based on a preset rule; and generating the data model based at least on the object type.

Abstract: A method and apparatus for processing a table, a device, a storage medium and a product. An implementation of the method comprise: receiving a content query request for a target table; acquiring a target tree structure of the target table according to the content query request; where, the target tree structure is obtained by performing absorbing processing and merging processing on at least one target cell in the target table; acquiring to-be-queried content in the content query request; and querying target content matching the to-be-queried content from the target tree structure.

Abstract: The disclosure discloses a method and an apparatus for recognizing an entity word. The method includes: obtaining an entity word category and a document to be recognized; generating an entity word question based on the entity word category; segmenting the document to be recognized to generate a plurality of candidate sentences; inputting the entity word question and the plurality of candidate sentences into a question-answer model trained in advance to obtain an entity word recognizing result; and obtaining an entity word set corresponding to the entity word question based on the entity word recognizing result.

Abstract: Embodiments of the present disclosure provide a method, an apparatus and a device for generating entity relationship data, and a storage medium. The method includes: obtaining webpage source data corresponding to a target webpage; identifying at least one key value block from the webpage source data, wherein the key value block comprises at least one key value pair; identifying body values corresponding to the at least one key value block from the webpage source data; and generating entity relationship data corresponding to the target webpage according to the key value blocks and the body values corresponding to the key value blocks. With the technical solution the present disclosure, the webpage universality may be improved, labor cost may be reduced, and output quantity of the entity relationship data may be increased.

Abstract: A method for an industry text increment, as well as an electronic device and a computer readable storage medium for the same are provided. The method may include: acquiring an original industry text in a target industry field, an order of magnitude of a number of the original industry text being smaller than a preset first order of magnitude; and performing a sample incremental processing on the original industry text by using a distant supervision method, to obtain increased industry texts, an order of magnitude of a number of the increased industry texts is greater than a preset second order of magnitude, wherein the preset second order of magnitude is not smaller than the preset first order of magnitude.

Abstract: The present disclosure provides a medical data verification method, apparatus and an electronic device, related to a field of artificial intelligence technologies, such as AI (artificial intelligence) medical treatment, deep learning, knowledge graphs, natural language processing. A specific implementation is: obtaining medical data to be verified and a candidate document; obtaining feature vectors respectively corresponding to the medical data to be verified and the candidate document by processing the medical data to be verified and the candidate document by using a nature language processing model; obtaining N correlation vectors by calculating correlation between the medical data to be verified and the candidate document based on the feature vectors by using N methods, N being a positive integer greater than 1; and determining a confidence degree of the medical data to be verified to the candidate document by performing fusion calculation on the N correlation vectors.

Abstract: A medical fact verification method and apparatus, an electronic device, and a storage medium are provided. The medical fact verification method comprises: acquiring a medical fact to be verified and candidate evidence, wherein the medical fact to be verified includes a target entity, a target attribute and a target attribute value; inputting the target entity, the target attribute value and the candidate evidence into an attribute decision model to obtain a decision attribute; inputting the target entity, the target attribute value and the candidate evidence into a relevancy decision model to obtain a relevancy of the candidate evidence in a case that the target attribute and the decision attribute are the same; and determining that the medical fact to be verified is correct in a case that the relevancy of the candidate evidence accords with a preset condition.

Abstract: A method, apparatus, device, and storage medium for constructing a knowledge graph, relates to the field of data processing, and specifically to artificial intelligence technology is provided. The method may include: determining a scene and a scene element of the scene; determining a target tag from attribute tags based on an association relationship between an entity and the scene element, and an association relationship between the entity and each of the attribute tags; and establishing an edge between a scene node and a target tag node, to obtain a knowledge graph including scene information.

Abstract: Provided are a method, an apparatus and an electronic device for determining a knowledge sample data set, the method includes: acquiring a preset number of SPO triplet formats and source texts; acquiring, according to the SPO triplet formats, n SPO entries corresponding to the SPO triplet formats; searching, in the source texts, m first texts that match the n SPO entries, and generating a first knowledge sample data set; determining k second texts that meet the SPO triplet formats from the m first texts and generating a second knowledge sample data set; generating a target knowledge sample data set according to the first knowledge sample data set and the second knowledge sample data set. In the embodiments, the knowledge sample data set is automatically generated, the volume generation speed is fast, the cost is low, and the data size that can be produced is large, thus meeting the training requirement.

Abstract: Provided by the present disclosure is a new category tag mining method, involving the field of knowledge graph technology, and including: obtaining a plurality of queries during a current preset time period; labeling a category tag on each query of the plurality of queries, by using a pre-trained sequence labeling model, to extract the category tag currently corresponding to the query from the query; and removing a category tag already existing in a preset current category tag library from category tags currently corresponding to all the queries, and determining a remaining category tag as a new category tag. The present disclosure also provides an electronic device and a non-transitory computer-readable storage medium.

Abstract: The disclosure discloses a method and an apparatus for recognizing an entity word. The method includes: obtaining an entity word category and a document to be recognized; generating an entity word question based on the entity word category; segmenting the document to be recognized to generate a plurality of candidate sentences; inputting the entity word question and the plurality of candidate sentences into a question-answer model trained in advance to obtain an entity word recognizing result; and obtaining an entity word set corresponding to the entity word question based on the entity word recognizing result.