Abstract
With the rapid development of the Internet, the number of Internet users has grown rapidly, and the Internet has become more and more influential on people's lives. As a result, the amount of network text is increasing rapidly, and it is difficult to extract interested event information from it only by manual reading. Therefore, event extraction technique automatically extracting useful information from a large amount of unstructured texts becomes increasingly important. Event detection is the first step of event extraction task and plays a vital role in it. However, current event detection research lacks comprehensive consideration of the context of the trigger words. A Chinese event detection method based on multi-feature fusion and BiLSTM is proposed in this paper. The contextual information of word is divided into sentence-level and document-level in the method. The contextual information is captured based on BiLSTM model. At the same time, a word representation method suitable for trigger word classification tasks is proposed in this paper. The word representation incorporates semantic information, grammar information, and document-level context information of word. The word vectors in the sentence are sequentially inputted into BiLSTM model to obtain output vectors containing sentence-level contextual information. Finally, output vectors of BiLSTM are inputted into the Softmax classifier to realize the identification of the trigger words. The experimental results show that Chinese Event Detection Based on Multi-feature Fusion and BiLSTM method proposed in this paper has high accuracy.
Highlights
With the rapid growth of Internet data, there is an urgent need for a technology that can automatically find and extract useful information from massive data
The contextual features in the process of the trigger words recognition are fully considered, and a Chinese event detection method based on multi-feature fusion and BiLSTM is proposed
The Chinese event detection method based on multi-feature fusion and BiLSTM is described in detail
Summary
With the rapid growth of Internet data, there is an urgent need for a technology that can automatically find and extract useful information from massive data. The event extraction mainly consists of two steps: the first step is the detection of the event, that is, the identification and classification of the trigger word; the second step is to analyze the identified event and extract the event elements. Event detection faces certain challenges, mainly due to the complexity of the trigger words recognition tasks. The identification of the trigger words needs to consider the semantic relationship between each sentence, that is, the context information at the document level. As shown in the first sentence of Example 4, the sentence contains the trigger word ‘‘ (leave)’’, but it is difficult to derive the type of event. The contextual features in the process of the trigger words recognition are fully considered, and a Chinese event detection method based on multi-feature fusion and BiLSTM is proposed. The proposed method is summarized and the research direction is introduced
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