Research On Event Extraction Methods For Chinese Text

With the rapid development of computer technology and the scale of the Internet,it is particularly important to extract useful information from the growing mass of network information and present it in the form of structured text,and information extraction technology has attracted much attention as a solution to deal with this problem.Among them,event extraction is an important research direction in the field of information extraction and one of the most challenging tasks in the field of information extractionEvent extraction is to extract the events that users care and are interested in from the massive unstructured text described in natural language,and then save and display them in a structured form to facilitate users to quickly obtain the core information of the events and grasp the corresponding progress of the events.The event extraction task can be divided into two sub-tasks from the processing flow,which are event trigger word extraction and event element extraction.There are problems in traditional event extraction methods such as easy to ignore contextual information and inadequate extraction of key features of text.In order to solve the above problems,this paper investigates the event extraction of Chinese text using deep learning methods,and the specific research work is as follows:1.A recurrent neural networks-based Chinese event trigger word detection method is proposed for the problem of Chinese event type recognition and classification.In this paper,we use the BERT model to pre-train the Chinese corpus to obtain the BERT pretrained word vectors,and use the LTP tool to analyze and process the sentence to obtain the lexical vectors,semantic dependency vectors and syntactic analysis vectors of words.The four text vectors are stitched together as the input to the Bi LSTM network layer.The information feature representation of the text is calculated by the Bi LSTM network layer,and the detection results of event-triggered words are finally obtained after processing by the CRF sequence annotation layer.2.An event element detection method combining event trigger word information and attention mechanism is proposed for the recognition and classification of Chinese event elements.Using the type information and corresponding location information of the event trigger word,the BERT pre-trained word vector and lexical vector are spliced with the trigger word type vector and trigger word location vector,and the text vector obtained after splicing is used as the input of the Bi LSTM network layer,and the attention layer is added on top of the Bi LSTM network layer to better obtain the event element information around the trigger word,the Softmax layer is used as the output to get the detection results of event elements.3.A joint model-based event extraction method is proposed for accomplishing the task of detecting event types and event elements in parallel.In this paper,syntactic analysis vectors and semantic dependency vectors are combined with BERT pretraining vectors and lexical vectors as the input of the neural network layer.After the text feature extraction of the input text vectors by the Bi LSTM network layer,the feature information is used as the input of the GCN network layer,and the next step of text feature extraction is performed using the GCN network layer,and the classification work is completed by the two trained classifiers of the joint detection layer after the GCN completes the corresponding computation process.