Research On Chinese Emergency Event Extraction Algorithm Based On Deep Learning

With the increase of Internet users,the data generated by the interaction between users gradually accumulates,and electronic text information presents an explosive development in the Internet.When emergencies occur,a lot of relevant event information will be published on the Internet.How to extract structured emergency information from massive texts has also become an important aspect of network public opinion analysis.There are two main problems in the current emergency event extraction methods: First,in the trigger word extraction method,the importance of the contextual semantic information of the text cannot be fully utilized for extraction.Second,in the argument extraction method,the dependent syntactic information in the sentence cannot be fully utilized for extraction.In view of the limitations of the current method,this thesis proposes a Chinese emergency event extraction algorithm based on deep learning.In order to facilitate the optimization of each module,the event extraction is carried out in a pipeline manner,and the event extraction is divided into triggers based on the multi-head attention mechanism.A word extraction model and an argument extraction model that incorporates syntactic information of dependencies.The main research contents are as follows:(1)A trigger word extraction model based on multi-head attention mechanism is proposed.The pre-trained model BERT-wwm is used as the text representation.The model is trained in the way of full word coverage for Chinese-specific grammatical expressions,and can learn the semantic information of Chinese words.In order to obtain the global features of the text,BiLSTM is used to extract the forward and backward semantic information of the text.The multi-head attention mechanism is introduced for semantic information extraction.The multi-head attention mechanism can enhance the more important features of trigger word extraction,weaken the irrelevant or unimportant features,make the feature distribution more reasonable,and can capture from different semantic spaces.Text features to enhance text expression.Considering the correlation between adjacent nodes,the results are marked by means of CRF joint decoding.(2)An argument extraction model that fuses dependent syntactic information is proposed: Since there is a certain relationship between trigger words and arguments,the location features of trigger words obtained by the trigger word extraction model and the pre-training model BERT-wwm are fused as text representations,and then combined with BiLSTM concatenation for contextual semantic information extraction.In order to obtain the dependencies between the components of each language unit in a sentence,a dependency syntax tree about emergent events is constructed,the constructed dependency tree is converted into an adjacency matrix,and this matrix is fused with a graph attention network.The graph attention network models the syntactic adjacency matrix,then learns the dependency syntax in the text,and pays attention to the dependency syntax information that is more important for argument extraction through the attention mechanism.Finally,the result is marked by CRF joint decoding.This thesis conducts experiments on the Chinese Emergency Corpus(CEC)constructed by the Institute of Semantic Intelligence of Shanghai University,compares the research results with the current advanced models,and conducts ablation analysis on each module of the model.The experimental results show that the extraction results of the event trigger word extraction model and event argument extraction model proposed in this thesis are better than the comparison model in terms of precision rate,recall rate and F1 value,which proves the feasibility and effectiveness of the model in this thesis.