Baidu Online Network Technology (Beijing) Co., Ltd.

Beijing Baidu Netcom Science and Technology Co., Ltd.

Beijing Baidu Netcom Science Technology Co., Ltd

Jingbo Zhou

Hui Xiong

Yuan Xia

Haishan Wu

Jianguo Duan

Airong Jiang

Wei Fan

Yanyan Li

Renjun Hu

Weishan Dong

Shuangli Li

Chao Li

Jizhou Huang

Fan Wang

Dejing Dou

Yehui Yang

Tong Xu

Mengwen Xu

Yixiong Xiao

Hongliang Fei

Chaochun Liu

Hongbin Pei

A method for acquiring point of interest (POI) representation information, includes: acquiring a POI relationship graph, and acquiring a node relationship set of each node on the POI relationship graph based on the POI relationship graph, in which the POI relationship graph includes edges representing relationships between nodes of POIs, and the node relationship set includes relationship paths where the node reaches all second-order neighbor nodes via any one first-order neighbor node; acquiring a spatial distance matrix between the node and corresponding second-order neighbor nodes; and determining first POI representation information of the node based on the node relationship set of the node and the spatial distance matrix.

Method and device for acquiring point of interest representation information, and method for training spatial relationship perception model for points of interest

Data query method and apparatus based on large model, an electronic device, and a storage medium are disclosed, which relates to the field of artificial intelligence, specifically in natural language processing, deep learning, and large model technologies, applicable to scenarios such as dialogue systems and information retrieval. The method includes: performing entity recognition on a query to obtain the target entity in the query; obtaining a first related content associated with the target entity from internal information, and performing data analysis on the first related content using a large language model (LLM) to obtain a data analysis result; obtaining a second related content associated with the target entity from external information, and performing data generation on the second related content using the LLM to obtain a data generation result; obtaining a query result corresponding to the query based on the data analysis result and the data generation result.

Data query method and apparatus based on large model, electronic device, and storage medium

A method is provided. The method includes: determining, by one or more computers, a name of a target region, wherein the name of the target region is determined based on geometry attribute information of the target region; and determining, by one or more computers, region attribute information of the target region based on the name of the target region.

Method for determining region attribute information, computing device, and storage medium

The present disclosure provides a method for optimizing a tag of a point of interest s(POI). The method includes: obtaining first portrait feature data of each POI in a plurality of POIs and second portrait feature data of each tag in a plurality of marked tags corresponding to the plurality of POIs; mapping the first portrait feature data of each POI and the second portrait feature data of each tag to a metric space to obtain a first feature vector of each POI and a second feature vector of each tag; and optimizing at least one marked tag corresponding to a target POI based on a vector similarity between a first feature vector of the target POI and a second feature vector of at least one tag. The present disclosure provides an apparatus for optimizing a tag of a POI, an electronic device and a computer readable medium.

Method and apparatus for optimizing tag of point of interest, electronic device and computer readable medium

A method for determining a competitive relation of Points of Interest (POI), and a device are provided in the present disclosure. The specific implementation includes: determining POI representation data between two target POIs based on service-related data of the target POIs; and determining a competitive relation between the target POIs based on the POI representation data.

Method for determining competitive relation of points of interest, device

A method and apparatus for mining a competition relationship between POIs. An embodiment of the method includes: acquiring a graphlet mining result obtained by mining map retrieval data of users which encompasses attribute information of retrieved target POIs, the graphlet mining result encompassing occurrence frequencies of respective preset situations, and a preset situation comprising: conforming to attribute information of POIs represented by a corresponding preset graphlet and a preset association relationship between attribute information of at least two POIs; for a first and second POI, determining an occurrence frequency of a preset situation corresponding to a preset graphlet where attribute information of the first and second POI co-occur, and generating a relationship feature of the first and second POI; and inputting the relationship feature into a pre-trained relationship prediction model to obtain a competition relationship prediction result of the first POI and the second POI.

Method and apparatus for mining competition relationship POIs

Embodiments of the present disclosure provide a method and apparatus for generating information. A method may include: determining, according to received positioning request information, visiting information for a target area of interest, the visiting information including location information of at least one visiting point; determining, according to the location information of the at least one visiting point, a visiting point distribution map including the at least one visiting point; performing grid division on the visiting point distribution map, to obtain a first grid map including at least one grid; and generating, based on the first grid map, outline information for the target area of interest.

Method and apparatus for generating information

Embodiments of the present disclosure provide a method and apparatus for optimizing a tag of a point of interest. The method includes: acquiring a set of points of interest and a set of tags of points of interest in the set of points of interest; generating a point of interest-tag matrix based on the set of points of interest and the set of tags of points of interest in the set of points of interest; extracting a feature of a point of interest-tag in the point of interest-tag matrix; inputting the feature of the point of interest-tag in the point of interest-tag matrix into a pre-trained ternary self-adaptive collaborative learning model, to obtain a point of interest-tag score matrix; and optimizing the set of tags of points of interest in the set of points of interest based on the point of interest-tag score matrix.

Method and apparatus for optimizing tag of point of interest

A method, computer device and storage medium for mining a point of interest competitive relationship are disclosed. The method includes: for a first POI to be processed, obtaining a set of second POIs serving as mining objects of the first POI; for each second POI in the set, forming a POI pair with the second POI and the first POI, determining a relationship evaluation index of the POI pair according to user's search operations on a map for POIs, and judging whether the two POIs in the POI pair are in a competitive relationship according to the relationship evaluation index. The technical solution of the present disclosure may be applied to improve the accuracy of the processing results.

Method, computer device and storage medium for mining point of interest competitive relationship

A method and apparatus for generating information. A specific embodiment of the method includes: acquiring a set of geographic information point sequences and a set of identifiers comprising an identifier of each geographic information point sequence in the set of the geographic information point sequences; for the each geographic information point sequence in the set of the geographic information point sequences, clustering geographic information points in the each geographic information point sequence and generating an element sequence corresponding to the each geographic information point sequence; learning, by utilizing a machine learning method based on the set of identifiers and the generated element sequences, to obtain a matrix for the identifiers in the set of identifiers; and generating, for each identifier in the set of identifiers, based on the identifier and the matrix, information of a user to which the geographic information point sequence indicated by the identifier belongs. The present embodiment realizes a targeted information generation.

The present disclosure provides a method and apparatus for building a text classification model, and a text classification method and apparatus. The method of building a text classification model comprises: obtaining a training sample; obtaining a vector matrix corresponding to the text, after performing word segmentation for the text based on an entity dictionary; using the vector matrix corresponding to the text and a class of the text to train a first classification model and a second classification model respectively; during the training process, using a loss function of the first classification model and a loss function of the second classification model to obtain a loss function of the text classification model, and using the loss function of the text classification model to adjust parameters for the first classification model and the second classification model, to obtain the text classification model formed by the first classification model and the second classification model. The text classification method comprises: obtaining a to-be-classified text; obtaining a vector matrix corresponding to the text, after performing word segmentation for the text based on an entity dictionary; inputting the vector matrix into a text classification model, and obtaining a classification result of the text according to output of the text classification model. The text classification effect can be improved through the technical solutions of the present disclosure.

Method and apparatus for building text classification model, and text classification method and apparatus

A road detection method and apparatus. A specific embodiment of the method includes: acquiring an image of a predetermined region; semantically segmenting the image to acquire a first probability that a region corresponding to each pixel in the image is a road region; acquiring a historical position information set of a target terminal; correcting, in response to historical position information existing in the historical position information set, the historical position information indicating a historical position located in the predetermined region, the first probability according to the historical position information to obtain a second probability; and determining a region corresponding to a pixel having the second probability greater than a preset threshold as a road region. Such an embodiment improves the road detection accuracy.

Road detecting method and apparatus

A method and apparatus for outputting information are provided. A specific embodiment of the method comprises: retrieving at least one medical entity keyword and at least one attribute keyword from a target medical text; then generating a set of keyword pairs, each of the keyword pairs including the retrieved medical entity keyword and the retrieved attribute keyword; then retrieving, for each of the keyword pairs in the set of keyword pairs, a text characteristic of the each of the keyword pairs in the target medical text, and introducing the retrieved text characteristic into a pre-trained association-relationship determination model to obtain an association result corresponding to the each of the keyword pairs; and finally outputting the keyword pairs having the association relationship in the set of keyword pairs. The embodiment improves the abundance of outputted information contents.

Method and apparatus for outputting information

A method and apparatus for pushing information. A specific embodiment of the searching method comprises: analysing, based on a second search operation sequentially performed to a map application installed on a client by a user, a search term corresponding to the second search operation; matching the analysed search term with a first search term in a first search term sequence to determine whether the matching is successful; acquiring log data of the map application installed on the client, in response to the matching being successful; determining a vehicle demand probability of the user based on the log data, acquired feature information of the user and a pre-trained vehicle demand probability model; and pushing information to the client in response to the determined vehicle demand probability being greater than a preset vehicle demand probability threshold.

Method and apparatus for pushing information

The present application discloses a risk early warning method and apparatus. An implementation of the method includes: monitoring, in real time, search traffic for a predetermined location from users using an online map within a preset period; determining whether the search traffic exceeds a preset search traffic threshold; and sending early warning information of a crowd gathering risk if the search traffic exceeds the preset search traffic threshold. The implementation effectively utilizes the map search traffic capable of reflecting the intention of users and realizes the early warning for the crowd gathering risk.

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Beijing, China

Jingbo Zhou