Method and system for news event extraction based on neural network

Abstract

The invention discloses a method and system for news event extraction based on neural network. The method comprises the steps of: conducting data pre-processing on original text of training corpus; introducing an event sentence sequence represented by a word vector into a bidirectional long and short memory network, using the bidirectional long and short memory network to train and obtain semantic features of each candidate trigger word; introducing the event sentence sequence represented by a word vector into a convolutional neural network, using the convolutional neural network to train and obtain global features of the event sentences where the candidate trigger words are in; according to the semantic features of candidate trigger words and the global features of the event sentences where the candidate trigger words are in, using softmax as a classifier to classify each candidate trigger word, and therefore finding out the trigger words for news events, and according to the trigger word type, judging the type of the event. The method and system can quickly and accurately extract news events and deal with news events contained in non-standard statements, and has the advantages of high efficiency and universal applicability.

Description

technical field [0001] The present invention relates to natural language processing, in particular to a news event extraction method and system based on the combination of bidirectional long-short-term memory network (BiLSTM) and convolutional neural network (CNN). Background technique [0002] With the development of computers and the increasing popularity of the Internet, a large amount of information appears in front of people in the form of electronic text. In a large number of network texts, how to discover valuable news events has become an urgent problem to be solved, and event extraction is produced under this background. As a subtask of information extraction, event extraction is a research hotspot in information extraction, and its research content is to automatically discover specific types of events and their event elements from natural texts. [0003] Extracting corresponding events from text is usually realized by identifying trigger words of events, so trigge...

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Problems solved by technology

[0005] Based on the above research status, there are mainly the following problems in the extraction of news events: First, the discrimination of news events mainly depends on the trigger words themselves, ignoring the contextual relationship. When encountering ambiguous candidate trigger words, it is easy to cause the event category Misjudgment

Second, network texts, especially Weibo texts, are mostly irregular sentences, and current event extraction methods lack research on extracting events from irregular sentences.

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