Research And Application Of Event Extraction Based On Long Short-Term Memory Networks

In recent years,with the rapid development of the Internet,a large amount of information has been presented to people in the form of electronic text.Facing the age of information explosion,how to extract interesting information from unstructured text has become one of the research hotspots,and information extraction technology came into being at the historic moment.Event extraction is an important part of information extraction technology and one of the most challenging tasks in the field of information extraction.The purpose of event extraction is to extract key elements describing the event from unstructured text data and show them structurally.The current event extraction technology is mainly based on the supervised method,which is highly dependent on high-quality corpus annotation.Event extraction can be divided into two stages: event trigger extraction and event argument extraction.The research of event extraction technology has important guiding significance for natural language processing tasks such as knowledge mapping,public opinion analysis,automatic summarization and machine translation.Most of the traditional event extraction methods have insufficient key feature extraction,and it is easy to ignore the context information.In order to solve the above problems,based on the long short-term memory network,this paper introduces the convolution operation and attention mechanism,and conducts a series of events related research.The main research contents of this paper include:1?This paper proposes an event trigger extraction method based on convolutional long short-term memory network.The word embedding and position feature are selected as the representation of text vectorization.Word embedding adopts Skip-gram model,and the position feature adopts a discretized 5-dimensional vector.To solve the problem of event classification error caused by polysemy in the traditional event extraction method,the long short-term memory neural network is used to extract the sentence-level features,the convolution operation is used to extract the lexical-level features,and the two features are combined into the output layer to predict the results of event trigger extraction.In the model training stage,the cross entropy cost function is used as the loss function,and the gradient descent method uses the Adam optimization algorithm.Except the standard event trigger extraction results,this paper also verifies the validity of the model on the fuzzy event trigger extraction.2?Based on the long short-term memory network,this paper introduces the attention mechanism and builds the event argument extraction model based on the Encoder-Decoder framework.In the feature selection,in addition to the word embedding and the position feature,the event trigger word type feature is selected as the representation of the text vectorization.In the construction of the event argument extraction model,the attention layer can effectively show the impact of event trigger words and other event arguments on the candidate event arguments.This solves the problem that when an event sentence contains multiple events,the event argument role is easy to identify confusion,which improves the efficiency of extracting event arguments.3?This paper applies the event extraction model to the field of news public opinion,and realizes the prototype news public opinion event extraction system.The prototype news public opinion event extraction system uses the Conv-BiLSTM model and the AB-BiLSTM model in the two stages of specific event trigger and event argument extraction,and completes the event extraction in a pipelined approach.In order to solve the problem that the training corpus is insufficient in the actual application process and cannot adapt to the new field,this paper designs an incremental learning framework,which enhances the robustness and universal applicability of the system.In the implementation of the event specific display,this paper also designed the event timeline function and event map function from different dimensions of time and space,so that the prototype news public opinion event extraction system are ordered,systematic and complete.