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News sub-event prediction method and apparatus based on depth learning technology

A prediction method and sub-event technology, applied in the field of intelligence analysis, can solve the problems of not considering the sequence structure, not considering the use of topic information, and only predicting.

Inactive Publication Date: 2017-09-01
TSINGHUA UNIV
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Problems solved by technology

[0005] However, the above-mentioned news sub-event prediction techniques all have the following problems: 1. Artificial features are required to represent events and can only predict events that exist in the training set; 2. The sequence structure of events at different granularities is not considered: Words describing a specific sub-event form a sequence, and sub-events belonging to the same event form a sequence; 3. No consideration is given to utilizing topic information existing in existing sub-events

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  • News sub-event prediction method and apparatus based on depth learning technology
  • News sub-event prediction method and apparatus based on depth learning technology
  • News sub-event prediction method and apparatus based on depth learning technology

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Embodiment Construction

[0020] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0021] The present invention proposes a new model for sub-event prediction, that is, Hierarchical Long Short-Term Memory (HLSTM) hierarchical long-short-term memory model. The input of the model is text describing observed sub-events (such as news headlines), and the output is a short text describing future sub-events. HLSTM is a hierarchical LSTM structure with two layers. The first layer is used to model sub-events. It sequentially takes each word in the description text of the sub-event as input, and finally represents the sub-event as a vector. The second layer is used to model the observed sequence of sub-events, while considering the topic information to enhance t...

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Abstract

The present invention provides a news sub-event prediction method and apparatus based on the depth learning technology. The method comprises: step 1: carrying out representation learning on the sub-event by using the LSTM depth learning technology based on the pre-processed large-scale sub-event sequence data; Step 2, carrying out representation learning on the sub-event sequence based on the sub-event representation learning and sub-event subjects; and step 3, predicting the next sub-event by using the sub-event sequence representation obtained in the step 2. According to the method provided by the present invention, the sub-event prediction function can be effectively realized, the sub-event not existing in the training corpus can be predicted, and the better prediction effect can be obtained.

Description

technical field [0001] The present invention relates to the technical field of intelligence analysis, and more specifically, to a method and device for predicting news sub-events based on deep learning technology. Background technique [0002] At present, a news event refers to one or a series of events that occur at a specific time and place, and a news event is usually composed of a series of ordered sub-events. For example, an earthquake event usually includes a series of sub-events such as earthquake occurrence, earthquake casualties, earthquake financial losses, post-earthquake rescue, post-earthquake donation assistance, and post-earthquake disaster area reconstruction. A sub-event is usually described by a news report. [0003] So far, thousands of news events and their sub-events have been reported and recorded. It is possible to predict the next sub-event of a series of sub-events by exploiting large-scale historical events. Through the above-mentioned scientific...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/27G06N3/08
CPCG06F40/30G06N3/08
Inventor 李涓子胡琳梅
Owner TSINGHUA UNIV
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