# An Event Extraction Approach Based on a Multi-Round Q&A Framework
###### Abstract  
Event extraction aims to present unstructured text containing event information in a structured form to help people quickly mine the target information. Most of the traditional event extraction methods focus on the design of complex neural network models, which rely on a large amount of annotated data to train the models. In recent years, some researchers have proposed the use of machine reading comprehension models for event extraction; however, the existing methods are limited to the single-round question-and-answer model, ignoring the dependency relation between the elements of event arguments. In addition, the existing methods do not fully utilize knowledge such as a priori information. To address these shortcomings, a multi-round Q&A framework is proposed for event extraction, which extends the existing methods in two aspects: first, by constructing a multi-round extraction problem framework, the model can effectively exploit the hierarchical dependencies among the argument elements; second, the question-and-answer framework is populated with historical answer information encoding slots, which are integrated into the multi-round Q&A process to assist in inference. Finally, experimental results on a publicly available dataset show that the proposed model achieves superior results compared to existing methods. event extraction; multi-round Q&A framework; historical answer information; machine reading comprehension +
Footnote †: journal:  
**Example 1**. : _British anti-terror police arrested [PERSON] in London on May 2._  
First, in the event type detection task, we determine \"arrested\" as the trigger word in the sentence and determine the event type as Justice and Arrest-Jail, according to the trigger word. According to the event type, the corresponding event elements \"British anti-terror police\", \"[PERSON]\", \"London\", and \"May 2\" were extracted and their corresponding roles were identified as Agent, Person, Time, and Place. In research work on event extraction tasks, early approaches were based on pattern matching, which required significant human resources and had poor portability and low recall when migrating to new domain data because of its dependence on the specific form of the text. Therefore machine-learning-based methods gradually replace the traditional pattern matching methods. More typical machine learning methods include maximum entropy models, conditional random fields, support vector machines, etc. The core of these methods lies in obtaining feature representations based on datasets and models and classifying tasks into classification problems for processing. However, the performance of these methods is greatly affected by the features and can cause the problem of error accumulation. In recent years, most of the event extraction studies have been based on deep learning methods and progress with the evolution of deep learning models. Compared with the earlier methods, neural network models solve the problem of difficulty improving the learning ability of traditional machine learning methods and can also use the chain network structure of long short-term memory neural networks to model the contextual relations, which does not depend on external resources and further enhances the performance. After the prevalence of word vector tools, innovative cross-domain research results have been introduced to become the mainstream event extraction methods, such as the JMEE [1] model based on graph convolutional networks to represent sentence dependencies as graphs to obtain deeper semantic features, the Joint3 EE [2] model that combines named entity recognition with trigger word and argument recognition tasks, and the MTL-CRF [3] model that treats event extraction as a sequence labeling task based on conditional random fields.