A Text Event Extraction Method Combining Sparse Coding and Structure Perceptron

Abstract

The invention discloses a text event extraction method combining sparse coding and a structure perceptron. It includes the following steps: 1) Construct text data into training samples according to ACE or RichERE specification annotations; 2) Use the extracted entities as candidate entities for event trigger words and event parameters to extract text features; 3) Further extract text distributed words Vector features, learn sparse coding features; 4) Use training samples and extracted text features to train structure-perceptron classifiers, and at the same time identify trigger words and parameters related to events in the text; 5) For new text data, go through step 1 Then input the structure perception machine classifier to extract text event information. The present invention utilizes the sparse coding expression of the distributed word vector features based on the neural network, and strengthens the text features. On the other hand, the structural perceptron model is used to simultaneously learn the identification of event trigger words and event participants, thereby obtaining better event extraction effect.

Description

technical field [0001] The invention relates to event extraction, in particular to a text event extraction method combining sparse coding and a structure perceptron. Background technique [0002] An event is a thing that occurs or appears. The event involves the entities (people, objects, etc.) participating in the event or affected by the event, and also involves all aspects of space and time in the universe. It is very important to understand events and their descriptions in text data, and event extraction is often a key component of applications such as machine reading, news summarization, information retrieval, and knowledge base construction. [0003] Usually, the goal of event extraction task is to extract event-related trigger words and participants (persons or objects) in the text. Current state-of-the-art methods for event extraction generally include three steps: first, use pre-trained named entity recognition tools to extract entities such as people, institutions...

AI Technical Summary

Problems solved by technology

However, most of these features require manual intervention, and the extraction process takes a long time and is not universal

In recent years, neural network and deep learning technology have become research hotspots, and unsupervised distributed word vector extraction methods in the NLP field are becoming increasingly common. This distributed word vector learning method is simple and general, and does not require manual labeling of data, but its drawbacks It does not have the interpretability and flexibility of common sparse expression features, so some studies have converted this distributed word vector into a sparse expression form that is easy to use in traditional NLP problems

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