Event Extraction via DMCNN in Open Domain Public Sentiment Information

Abstract

Event extraction (EE) is a difficult task in natural language processing (NLP). The target of EE is to obtain and present key information described in natural language in a structured form. Internet opinion, as an essential bearer of social information, is crucial. In order to help readers quickly get the main idea of news, a method of analyzing public sentiment information on the Internet and extracting events from news information is proposed. It enables users to quickly obtain information they need. An event extraction method was proposed based on Chinese language public opinion information, aiming at automatically classifying different types of public opinion events by using sentence-level features, and neural networks were applied to extract events. A sentence feature model was introduced to classify different types of public opinion events. To ensure the effective retention of text information in the calculation process, attention mechanism was added to the semantic information, and an effective public opinion event extractor was trained through CNN and LSTM networks. Experiments show that structured information can be extracted from unstructured text, and the purpose of obtaining public opinion event entities, event-entity relationships, and entity attribute information can be achieved.