Beijing Baidu Netcom Science Technology Co., Ltd

Tsinghua University

Beijing Baidu Netcom Science and Technology Co., Ltd.

Institute of Flexible Electronics Technology of Thu, Zhejiang

Ye Jiang

Yabing Shi

Yong Zhu

Chunguang Chai

Shuangjie Li

Yang Zhang

Yue Hua Zhang

Xue Feng

Zhou Fang

Fang Huang

Ying Chen

Lanlan Liu

Haoran Fu

Baicheng Zhang

Qian Li

Xunchao Song

Miao Yu

Zhengming Zhao

Jiandong Sun

Liqiang Yuan

Boyang Li

Bochen Shi

Kainan Chen

Tian Tan

Yatao Ling

Xuefeng Hao

Zhaoji Wang

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.

Method of processing triple data, method of training triple data processing model, device, and medium

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.

Extracting triplets from text with relationship prediction matrix, entity prediction matrix, and alignment matrix

A method and apparatus for generating a temporal knowledge graph, a device and a medium. An embodiment comprises: acquiring corpus including time information; performing multivariate data extraction on the corpus, multivariate data including an entity pair, an entity relationship and a target time interval of the entity relationship, the target time interval being used to indicate a valid period of the entity relationship; and generating a temporal knowledge graph based on the entity pair, the entity relationship and the target time interval of the entity relationship.

Method and apparatus for generating temporal knowledge graph, device, and medium

Provided are a method for preparing a microfluidic device, a microfluidic device and a microfluidic system. The method includes: providing a mold having a groove; injecting a liquid metal into the groove of the mold, and solidifying the liquid metal to obtain a solid metal; separating the solid metal from the mold; providing the solid metal with an electrode; providing a cladding layer on a surface of the solid metal provided with the electrode, such that the solid metal is wrapped by the cladding layer, and at least a part of the electrode extends outside the cladding layer, so as to obtain a preform; and fixing the preform in a substrate, melting the solid metal and extending at least a part of the electrode outside the substrate, to obtain the microfluidic device.

Microfluidic device and preparation method therefor, and microfluidic system

A laser transfer printing device and a laser transfer printing method are provided. The laser transfer printing device includes a laser, a beam expander, a beam splitter and a focusing module. The laser is configured to generate a laser beam. The beam expander is disposed on an optical path of the laser beam generated by the laser to expand the laser beam generated by the laser. The beam splitter is disposed on an optical path of the laser beam expanded by the beam expander to reflect the expanded laser beam to the focusing module. The focusing module is disposed on an optical path of the laser beam reflected by the beam splitter to focus the laser beam and project the focused laser beam to a predetermined transfer processing position.

Laser transfer printing device and method

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.

Method of extracting table information, electronic device, and storage medium

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.

Method and apparatus for processing table, device, and storage medium

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.

Method and apparatus for processing information

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.

Category tag mining method, electronic device and non-transitory computer-readable storage medium

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.

Method and apparatus for verifying medical fact

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.

Method and apparatus for recognizing entity word, and storage medium

An IGBT modeling method includes creating piece-wise line functions describing a collector-emitter voltage v, a collector current iand a gate-emitter voltage vof the IGBT during a switching-on transient based on an internal structure of the IGBT and transient processes of the IGBT. The IGBT modeling method further includes creating piece-wise line functions describing the collector-emitter voltage v, the collector current iand the gate-emitter voltage vof the IGBT during a switching-off transient based on the internal structure of the IGBT and the transient processes.

Growing community of inventors

Beijing, China

Ye Jiang