Abstract: Based on the research of news data in the field of political diplomacy, aiming at the pro- blems of extraction difficulty, low recall rate and accuracy rate of event extraction based on traditional pattern matching, and low accuracy rate of event extraction based on deep learning method in specific field, this paper proposes an event extraction method in the field of political diplomacy based on similar semantics and dependency syntax. The proposed method extends the event triggers by similarity calculation of semantic description, which lays a foundation for accurate recognition of event types. Furthermore, based on patterns, text dependency syntactic parsing is used to identify and extract event elements in political and diplomatic fields, so as to achieve a structured description of events. The accuracy of the extraction results is obviously better than that of the end-to-end event extraction model based on the deep neural network, and it can be used for reference and implementation in other specific fields. Finally, the main difficulties and application prospects of event extraction are discussed and summarized.

Key words: event extraction, meta-event, semantics, pattern matching, political diplomacy field