Method for extracting events from news
A news and event technology, applied in computer components, special data processing applications, instruments, etc., can solve problems such as flooding, inability to select and digest massive information, and information loss
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Embodiment 1
[0025] Such as figure 1 As shown, this embodiment provides a method for extracting events from news, and the method specifically includes the following steps:
[0026] S01. Obtain an original news data set related to a target topic; including a news ID, a news title and a news content.
[0027] S02. Extract the abstract of the news as the event, and perform numerical conversion on the news text respectively.
[0028] Numerical transformations include:
[0029] Step 2.1, training the doc2vec model: segment the news title and news content into words, for example, the result of word segmentation of "today's weather is really good" is "today", "day", "day", "qi", "true" , "OK"; use the news title and news content with good word classification, respectively train the doc2vec model of the title and the doc2vec model of the content, and save it locally;
[0030] Step 2.2. Convert text into vectors: For any new piece of news, first segment the title and content, and use the above-t...
Embodiment 2
[0039] Such as figure 2 As shown, this embodiment provides a method for extracting events from news. On the basis of the above embodiments, it further provides a specific method for determining the event that news belongs to in the news box according to the similarity. Correspondingly, the method specifically include:
[0040] S11. Obtain an original news data set related to the target topic; including news ID, news title and news content;
[0041] S12. Extracting the summary of the news as the related event, and numerically converting the news text respectively;
[0042] Numerical transformations include:
[0043] Step 2.1, training the doc2vec model: segment the news title and news content into words, for example, the result of word segmentation of "today's weather is really good" is "today", "day", "day", "qi", "true" , "OK"; use the news title and news content with good word classification, respectively train the doc2vec model of the title and the doc2vec model of the ...
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