Resolution of English Event Pronouns Based on Machine Learning

Abstract

As the basis of machine translation, anaphora aims to let the machine determine the entity or event to which the sentence refers by exploring the anaphora relationship between sentences. Prior to this, the research on anaphora resolution mainly focused on the resolution of entity anaphora. Through unremitting efforts, the elimination of entity-reference relationship has achieved great success, but the equally important event reference has been stagnant. This means that we can promote the development of machine translation by enhancing event reference. In this paper, a new method is proposed, which uses the latest machine learning algorithm to eliminate English event pronouns. Through feature extraction, data preprocessing, and the introduction of end-to-end double-loop neural network and attention mechanism, the network’s ability to acquire contextual features is improved, and finally, the purpose of eliminating English event pronouns is achieved. In the experimental part, this paper also conducts training and testing on the latest data set KBP. It is found that the model algorithm proposed in this paper can perform the task of experimental setup well, and the value of 40.3% F1 is given under CONLL evaluation index. This proves that the model can understand semantic information very effectively and extract relevant information from the given semantic information.