Method and apparatus for extracting name of POI, device and computer storage medium

A method for building a name ranking model, comprising:

• acquiring training samples comprising a positive example and a negative example of the name of a POI;
• extracting at least one of a search web page feature, a document statistical feature and a semantic feature from each training sample, and determining the probability of each training sample as the name of the POI;
• and
• performing a training operation with a pairwise algorithm to obtain the name ranking model with a training target of maximizing the difference between the probability of the positive example as the name of the POI and the probability of the negative example as the name of the POI,
• wherein extracting the search web page feature from the training sample comprises:
• performing a search with the training sample as a query;
• and
• performing a semantic coding operation on an acquired search result to obtain an implicit vector of the search result as the search web page feature of the training sample,
• wherein the performing a semantic coding operation on an acquired search result to obtain an implicit vector of the search result comprises:
• acquiring top N search result texts, N being a preset positive integer;
• performing a semantic coding operation on each acquired search result text to obtain an implicit vector of each search result text;
• and
• fusing the implicit vectors of the search result texts to obtain the implicit vector of the search result.

An electronic device, comprising:

• at least one processor;
• and
• a memory communicatively connected with the at least one processor;
• wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform a method for extracting the name of a point of interest (POI) comprising:
• acquiring two or more text fragments identified from image data of the POI;
• constructing two or more candidate names using the text fragments;
• and
• ranking the candidate names using a pre-trained name ranking model, and determining the name of the POI according to the result of the ranking;
• wherein the name ranking model determines the probability of each candidate name as the name of the POI using at least one of a search web page feature, a document statistical feature and a semantic feature extracted from each candidate name, and ranks the candidate names according to the probabilities,
• wherein extracting the search web page feature from the candidate name comprises:
• performing a search with the candidate name as a query;
• and
• performing a semantic coding operation on an acquired search result to obtain an implicit vector of the search result as the search web page feature of the candidate name,
• wherein the performing a semantic coding operation on an acquired search result to obtain an implicit vector of the search result comprises:
• acquiring top N search result texts, N being a preset positive integer;
• performing a semantic coding operation on each acquired search result text to obtain an implicit vector of each search result text;
• and
• fusing the implicit vectors of the search result texts to obtain the implicit vector of the search result.

An electronic device, comprising:

• at least one processor;
• and
• a memory communicatively connected with the at least one processor;
• wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform a method for building a name ranking model comprising:
• acquiring training samples comprising a positive example and a negative example of the name of a POI;
• extracting at least one of a search web page feature, a document statistical feature and a semantic feature from each training sample, and determining the probability of each training sample as the name of the POI;
• and
• performing a training operation with a pairwise algorithm to obtain the name ranking model with a training target of maximizing the difference between the probability of the positive example as the name of the POI and the probability of the negative example as the name of the POI;
• wherein extracting the search web page feature from the training sample comprises:
• performing a search with the training sample as a query;
• and
• performing a semantic coding operation on an acquired search result to obtain an implicit vector of the search result as the search web page feature of the training sample,
• wherein the performing a semantic coding operation on an acquired search result to obtain an implicit vector of the search result comprises:
• acquiring top N search result texts, N being a preset positive integer;
• performing a semantic coding operation on each acquired search result text to obtain an implicit vector of each search result text;
• and
• fusing the implicit vectors of the search result texts to obtain the implicit vector of the search result.

A non-transitory computer-readable storage medium storing computer instructions therein, wherein the computer instructions are used to cause the computer to perform a method for extracting the name of a point of interest (POI) comprising:

• acquiring two or more text fragments identified from image data of the POI;
• constructing two or more candidate names using the text fragments;
• and
• ranking the candidate names using a pre-trained name ranking model, and determining the name of the POI according to the result of the ranking;
• wherein the name ranking model determines the probability of each candidate name as the name of the POI using at least one of a search web page feature, a document statistical feature and a semantic feature extracted from each candidate name, and ranks the candidate names according to the probabilities,
• wherein extracting the search web page feature from the candidate name comprises:
• performing a search with the candidate name as a query;
• and
• performing a semantic coding operation on an acquired search result to obtain an implicit vector of the search result as the search web page feature of the candidate name,
• wherein the performing a semantic coding operation on an acquired search result to obtain an implicit vector of the search result comprises:
• acquiring top N search result texts, N being a preset positive integer;
• performing a semantic coding operation on each acquired search result text to obtain an implicit vector of each search result text;
• and
• fusing the implicit vectors of the search result texts to obtain the implicit vector of the search result.

A non-transitory computer-readable storage medium storing computer instructions therein, wherein the computer instructions are used to cause the computer to perform a method for building a name ranking model comprising:

• acquiring training samples comprising a positive example and a negative example of the name of a POI;
• extracting at least one of a search web page feature, a document statistical feature and a semantic feature from each training sample, and determining the probability of each training sample as the name of the POI;
• and
• performing a training operation with a pairwise algorithm to obtain the name ranking model with a training target of maximizing the difference between the probability of the positive example as the name of the POI and the probability of the negative example as the name of the POI,
• wherein extracting the search web page feature from the training sample comprises:
• performing a search with the training sample as a query;
• and
• performing a semantic coding operation on an acquired search result to obtain an implicit vector of the search result as the search web page feature of the training sample,
• wherein the performing a semantic coding operation on an acquired search result to obtain an implicit vector of the search result comprises:
• acquiring top N search result texts, N being a preset positive integer;
• performing a semantic coding operation on each acquired search result text to obtain an implicit vector of each search result text;
• and
• fusing the implicit vectors of the search result texts to obtain the implicit vector of the search result.