Computer-implemented method, device, and computer program product

An electronic device, comprising:

• at least one processor;
• and
• at least one memory storing computer program instructions, wherein the computer program instructions, when executed by the at least one processor, cause the electronic device to perform actions comprising:
• extracting respective themes of a set of documents with release time within a first period;
• determining respective semantic information of the themes and frequencies of the themes appearing in the set of documents utilizing at least a first machine learning model, the first machine learning model comprising a transformer-based model;
• and
• determining the number of documents associated with the themes within a second period according to a second machine learning model, different than the first machine learning model, the second machine learning model comprising a time sequence prediction model and based on the semantic information and frequencies of the themes, wherein the second period is after the first period;
• wherein the first machine learning model is configured to encode the semantic information of the themes and the frequencies of the themes appearing in the set of documents;
• wherein results of the encoding of the semantic information of the themes and the frequencies of the themes comprises, for each of at least a subset of the themes, (i) a semantic encoding component comprising at least a classification token for the semantic information of the theme, and (ii) a frequency encoding component comprising a plurality of frequency-related values generated for the frequency of appearance of the theme based on a designated probability function;
• wherein the second machine learning model is configured to process the semantic encoding components and the frequency encoding components to determine the number of documents associated with the themes within the second period;
• wherein determining the frequencies comprises determining a time sequence of frequency representations of the themes within the first period;
• wherein determining the time sequence of frequency representations comprises:
• for a time interval point within the first period, determining a frequency representation of the time sequence of frequency representations at the time interval point based on the number of documents corresponding to the themes;
• and
• wherein determining the frequency representation at the time interval point comprises:
• determining the frequency representation by using a position extending code based on the number of documents corresponding to the themes.

A computer program product tangibly stored in a non-transitory computer-readable storage medium and including machine-executable instructions, wherein the machine-executable instructions, when executed, cause a device to execute a method, the method comprising:

• extracting respective themes of a set of documents with release time within a first period;
• determining respective semantic information of the themes and frequencies of the themes appearing in the set of documents utilizing at least a first machine learning model, the first machine learning model comprising a transformer-based model;
• and
• determining the number of documents associated with the themes within a second period according to a second machine learning model, different than the first machine learning model, the second machine learning model comprising a time sequence prediction model and based on the semantic information and frequencies of the themes, wherein the second period is after the first period;
• wherein the first machine learning model is configured to encode the semantic information of the themes and the frequencies of the themes appearing in the set of documents;
• wherein results of the encoding of the semantic information of the themes and the frequencies of the themes comprises, for each of at least a subset of the themes, (i) a semantic encoding component comprising at least a classification token for the semantic information of the theme, and (ii) a frequency encoding component comprising a plurality of frequency-related values generated for the frequency of appearance of the theme based on a designated probability function;
• wherein the second machine learning model is configured to process the semantic encoding components and the frequency encoding components to determine the number of documents associated with the themes within the second period;
• wherein determining the frequencies comprises determining a time sequence of frequency representations of the themes within the first period;
• wherein determining the time sequence of frequency representations comprises:
• for a time interval point within the first period, determining a frequency representation of the time sequence of frequency representations at the time interval point based on the number of documents corresponding to the themes;
• and
• wherein determining the frequency representation at the time interval point comprises:
• determining the frequency representation by using a position extending code based on the number of documents corresponding to the themes.