Method and apparatus for acquiring poi state information

A method for establishing a POI state recognition model, comprising:

• acquiring training data, the training data comprising:
• a text and label annotation results of a POI name and a POI state comprised in the text;
• and
• taking the text as input to the POI state recognition model, and taking the label annotation results of the POI name and the POI state comprised in the text as target prediction results of the POI state recognition model, to train the POI state recognition model;
• wherein the POI state recognition model acquires a vector representation of each first semantic unit in the text, and acquires a vector representation of each second semantic unit in the text based on semantic dependency information of the text;
• fuses the vector representation of each first semantic unit and the vector representation of each second semantic unit to obtain a fusion vector representation of each first semantic unit;
• and predicts labels of the POI name and a POI state based on the fusion vector representation of each first semantic unit.

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 acquiring point of interest (POI) state information, wherein the method comprise:
• acquiring a text comprising POI information within a preset period from the Internet;
• and
• the text by using a pre-trained POI state recognition model, to obtain a two-tuple in the text, the two-tuple comprising a POI name and POI state information corresponding to the POI name;
• wherein the POI state recognition model acquires a vector representation of each first semantic unit in the text, and acquires a vector representation of each second semantic unit in the text based on semantic dependency information of the text;
• fuses the vector representation of each first semantic unit and the vector representation of each second semantic unit to obtain a fusion vector representation of each first semantic unit;
• and predicts labels of the POI name and a POI state based on the fusion vector representation of each first semantic unit.

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 acquiring point of interest (POI) state information, wherein the method comprises:

• acquiring a text comprising POI information within a preset period from the Internet;
• and
• recognizing the text by using a pre-trained POI state recognition model. to obtain a two tuple in the text, the two-tuple comprising a POI name and POI state information corresponding to the POI name;
• wherein the POI state recognition model acquires a vector representation of each first semantic unit in the text, and acquires a vector representation of each second semantic unit in the text based on semantic dependency information of the text;
• fuses the vector representation of each first semantic unit and the vector representation of each second semantic unit to obtain a fusion vector representation of each first semantic unit;
• and predicts labels of the POI name and a POI state based on the fusion vector representation of each first semantic unit.