This paper adopts the multimodal approach of human-computer collaboration to conduct an in-depth study and analysis of the practical teaching model of preschool education, and applies the designed model to the actual teaching process. The application of multimodal theory to preschool teaching is chosen to theoretically help expand the research scope of multimodal theory and enrich the research of preschool teaching, and practically help break through the previous single-modal teaching model, further enrich the theoretical guidance of preschool teaching, and improve the quality of preschool classroom teaching. Then, from the perspective of human-machine synergy, this paper analyzes the advantages of artificial intelligence technology and teachers in the English classroom, puts forward the new roles of teachers and learners in the human-computer cooperation teaching environment, and discusses the significance and value of applying the four main modules of human-computer cooperation teaching, human-computer gesture mapping and human-computer cooperation manipulator control in the preschool classroom. According to the physical structure of hand joints, the human hand joint angles are obtained through the inverse kinematic solution, and the human hand joint angles correspond to the dexterous manipulator one by one so that the dexterous manipulator can be controlled to imitate the human hand to complete flexible gesture movements and realize the vision-based collaborative human-machine control of the dexterous manipulator. Combined with Gagne’s nine teaching events, a model of the English teaching process based on human-computer collaboration was constructed. Based on this model, the “EasyDotWise English Teaching System” was designed to combine the basic lesson types of preschool classroom teaching and the secondary objectives of the English curriculum standards, including “reading text–reading aloud evaluation,” “playing speech–sound recognition,” and “presenting text–selection.” We designed and implemented three types of teaching activities: “reading text–reading aloud assessment,” “playing phonetic sounds–sound identification,” and “presenting text–comprehension selection.”

In recent years, overlapping entity relation extraction has received a great deal of attention and has made good progress in English. However, the research on overlapping entity relation extraction in Chinese still faces two key problems: one is the lack of datasets with overlapping entity instances, and the other is the lack of a neural network model that can effectively solve overlapping entity relation extraction. To address the above problems, this paper produces an interpersonal relationship dataset, NewsPer, for news texts and proposes a Chinese overlapping entity relation extraction model, DepCasRel. First, the model uses “Word-label” to incorporate the character features of Chinese text into the dependency analysis graph, and then uses the same binary labeling method to label the head and tail entities embedded in the text. Finally, the text’s triples are extracted. DepCasRel solves the problem that traditional methods make it difficult to extract triples with overlapping entities. Experiments on our manually annotated dataset NewsPer show that DepCasRel can effectively encode the semantic and structural information of text and improve the performance of an overlapping entity relation extraction model.

In today’s big data era, there are a large number of unstructured information resources on the web. Natural language processing researchers have been working hard to figure out how to extract useful information from them. Entity Relation Extraction is a crucial step in Information Extraction and provides technical support for Knowledge Graphs, Intelligent Q&A systems and Intelligent Retrieval. In this paper, we present a comprehensive history of entity relation extraction and introduce the relation extraction methods based on Machine Mearning, the relation extraction methods based on Deep Learning and the relation extraction methods for open domains. Then we summarize the characteristics and representative results of each type of method and introduce the common datasets and evaluation systems for entity relation extraction. Finally, we summarize current entity relation extraction methods and look forward to future technologies.