A method and device for generating an event relationship graph
A technology for generating events and maps, applied in the Internet field, can solve the problems of complex content and difficult to understand the subject of the event, and save reading time.
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Embodiment 1
[0047] see figure 1 , which is a flow chart of Embodiment 1 of the method for generating an event relationship graph provided by the present invention.
[0048] The method for generating the event relationship graph provided in this embodiment includes:
[0049] S101: splitting the manuscript into sentences according to preset punctuation marks;
[0050] It should be noted that, the method provided in this embodiment splits the manuscript into long sentences according to the splitting principle, that is, does not split sentences with punctuation marks such as commas, colons, and commas. For example, preset punctuation marks may include: period, exclamation point, question mark, and semicolon. A sentence with preset punctuation marks is a complete long sentence by default and can be split.
[0051] S102: Extracting the characters in the split sentences, and screening out the sentences containing the characters as backup sentences; the characters include at least one of names...
Embodiment 2
[0066] see image 3 , which is a flow chart of Embodiment 2 of the method for generating an event relationship graph provided by the present invention.
[0067] It should be noted that, in order to combine names, roles, and personal pronouns to remove duplicates, this embodiment also includes extracting the roles and personal pronouns corresponding to the names from the manuscript.
[0068] In addition, in order to enable readers to better understand the relationship between characters and roles, the present invention adds roles before the names of people when generating the event relationship graph, that is, the roles are used as attributives of the names of people, for example, judge Zhang XX. The judge is the attributive of Zhang Moumou.
[0069] In this embodiment, S201 is the same as S101 and will not be repeated here.
[0070] S202: Using natural language processing technology to perform word segmentation on the split sentence to obtain a word after word segmentation; ...
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