# Short Text Event Coreference Resolution Based on Context Prediction
## 2 Related Works  
ECR is one of the key subtasks in event extraction and fusion. However, early coreference resolution tasks mainly focused on the entity level. In the domain of entity coreference resolution, [PERSON] et al. [10] proposed an unsupervised algorithm pipeline for learning Disjunctive Normal Form (DNF) blocking schemes on Knowledge Graphs (KGs), as well as structurally heterogeneous tables that may not share a common schema. This approach aims to address entity resolution problems by mapping entities to blocks. Additionally, [PERSON] et al. [11] aim to enhance classifier performance metrics by incorporating the results of entity coreference analysis into the data preparation process for classification tasks. Due to the complexity of events themselves, as well as a lack of relevant language resources, research on ECR both domestically and abroad started relatively late and has developed more slowly than event extraction techniques. The ACE2005 corpus [12] was the first to add coreference attributes to event information, and in 2015, the Knowledge Base Population (KBP) [13] began related evaluation tasks, laying the foundation for subsequent research. Early research on ECR was mainly based on rule-based methods [14; 15; 16; 17; 18], but with the widespread application of machine learning methods. Recent research has mainly used traditional machine learning and neural network methods to complete ECR tasks [1]. A binary classifier is trained to determine whether two pieces of text refer to the same real-world event, and then coreferential events are optimized by clustering [19]. The differences in various methods mainly lie in the problem modeling, text feature extraction methods, and the internal details of the network model. Depending on whether manual annotation of language resources is required, ECR methods can be divided into three categories: supervised, semi-supervised, and unsupervised learning. Among them, supervised learning is the earliest and most widely used research approach. For example, [PERSON] et al. [20] designed a multi-layer CNN to extract event features, obtained deep semantic information, and further improved the performance of the coreference resolution algorithm by using multiple attention mechanisms. [PERSON] et al. [21] enhanced the representation of event text features using a Siamese network framework and used the Circle Loss loss function to maximize intra-class event similarity and minimize inter-class event similarity. [PERSON] et al. [22] trained a support vector machine (SVM) classifier based on more than 100 event features to determine whether the events refer to the same entity. Another attempt is made to utilize large amounts of out-of-domain text data. Due to the high cost of manual annotation, many scholars have attempted to use semi-supervised methods to study ECR tasks. The main idea is to use a small amount of labeled data to construct a learning algorithm to learn data distribution and features, thus completing the labeling of unlabeled samples. For example, [PERSON] et al. [23] achieved good results on a small-scale training dataset by using active learning to select information-rich instances. Similarly, [PERSON] et al. [24] employed active learning to select informative instances, indicating that only a small number of training sentences need to be annotated to achieve state-of-the-art performance in event coreference. Another attempt is made to utilize large amounts of out-of-domain text data [25]. Unsupervised learning methods completely eliminate the dependence on labeled data and are often probabilistic generative models. For example, [PERSON] et al. [26] constructed a generative, parameter-free Bayesian model based on hierarchical Dirichlet processes and infinite factorial hidden Markov models to achieve unsupervised ECR task learning. [PERSON] et al. [27] addressed the relatively scarce research on an unsupervised Chinese event coreference resolution task by proposing a generative model.