# An Open-Domain Event Extraction Method Incorporating Semantic and Dependent Syntactic Information
## 2 Related Work  
In recent years, researchers have conducted several studies on open-domain event extraction tasks and achieved good results. The existing methods are mainly based on clustering [4], lexicon [5], Bayesian [6], and syntactic analysis. Clustering-based methods mainly construct event patterns for each event cluster by event pattern induction using slot values. [PERSON] et al. [4] proposed a framework for media social event detection, a heterogeneous event-based information network and a novel GCN, based on which a parallel heterogeneous clustering algorithm (H-DBSCAN) was proposed for event detection and discovery.  
Many other researchers have proposed lexicon-based approaches applied to open-domain event extraction tasks using word and phrase vocabularies to support sequential event extraction tasks. For example, [PERSON] et al. [7] used patterns and shallow parsing to automatically construct a noun event extraction lexicon. [PERSON] et al. [5] proposed MONTEE, which is able to distinguish between different types of patterns. That is, to determine whether an event has occurred, has not occurred, or is uncertain, helping to avoid extracting untrue events. Most Bayesian-based approaches assume that documents are a joint distribution of different types of events, slots, entities, and contextual features. For example, [PERSON] et al. [6] proposed an open-event extraction model based on Bayesian and generative adversarial networks. Specifically, Dirichlet prior distributions and generators were used to identify potential event patterns. [PERSON] et al. [8] proposed a Bayesian model for extracting structured event representations from media, the latent event model (LEM), which is an unsupervised approach that does not require annotated data. [PERSON] et al. [9] proposed the deep latent variable model (DLVM), which is a neural topic model that was used to extract event types, patterns, and parameters with better performance than other basic models. However, most of these approaches ignore semantic and syntactic structural information.  
The results of syntactic analysis are often used to improve open-domain event extraction methods. For example, verbs help in event trigger detection, while nouns help in event parameter filtering. Syntactic dependencies, on the other hand, are useful for obtaining roles and parameters for the same event in multiple sentences. For example, [PERSON] et al. [10] used syntactic analysis, WordNet, and word disambiguation tools on a gasoline price prediction task to extract relevant events from the news to be input into a deep neural network. Research has shown that the utilization of dependent syntactic information in open-domain event extraction is significantly reduced due to the contextual features obtained by pre-trained encoding already containing certain structural information. However, approaches incorporating dependent syntactic information have been shown to be effective in improving the performance of tasks such as named entity recognition, biomedical event extraction tasks, and sentiment classification. For example, [PERSON] et al. [11] redefined named entity recognition as a structured prediction task based on the idea of dependency resolution in graphs. [PERSON] et al. [12] used dependency resolution to analyze dependency paths on a biomedical event extraction task to discover regularities in biological texts using syntactic patterns. Therefore, in order to obtain the best extraction results, not only semantic but also dependency syntactic information is needed. In this paper, we encode contextual features based on pre-trained models, learn semantic representations through the Bi-LSTM, use intermediate layer features to obtain syntactic information using dependent syntactic tools, and further realize modeling through DAGCNs with the gating mechanism used to dynamically fuse semantic information and semantically enhanced dependent syntactic information to obtain richer and more accurate features to improve the performance of open-domain event extraction.