Method for training keyword extraction model, keyword extraction method, and computer device

A computer device, comprising one or more processors and one or more memories, the one or more memories storing at least one piece of program code, the at least one piece of program code being loaded and executed by the one or more processors to perform a plurality of operations including:

• receiving an information entity, the information entity including a title and a text;
• performing word segmentation on the text, to obtain a plurality of candidate words;
• performing character segmentation on the title corresponding to semantics of the text, to obtain a plurality of characters;
• sequentially inputting the plurality of candidate words to a keyword extraction model, to obtain an attention weight of each candidate word relative to each of the plurality of characters, the attention weight being used for representing a degree of semantic association between the candidate word and the character;
• selecting, from the plurality of candidate words, a candidate word that appears in the title;
• determining an extraction threshold according to an attention weight of the selected candidate word relative to each of the plurality of characters;
• and
• determining a keyword of the text of the information entity from the candidate words according to the extraction threshold.

A non-transitory computer-readable storage medium, storing computer-readable instructions that, when executed by a processor of a computer device, cause the computer device to perform a plurality of operations including:

• receiving an information entity, the information entity including a title and a text;
• performing word segmentation on the text, to obtain a plurality of candidate words;
• performing character segmentation on the title corresponding to semantics of the text, to obtain a plurality of characters;
• sequentially inputting the plurality of candidate words to a keyword extraction model, to obtain an attention weight of each candidate word relative to each of the plurality of characters, the attention weight being used for representing a degree of semantic association between the candidate word and the character;
• selecting, from the plurality of candidate words, a candidate word that appears in the title;
• determining an extraction threshold according to an attention weight of the selected candidate word relative to each of the plurality of characters;
• and
• determining a keyword of the text of the information entity from the candidate words according to the extraction threshold.