# Short Text Event Coreference Resolution Based on Context Prediction
## 5 Experimental Results and Discussion  
Table 1 shows the performance of ECR-CP and ECR-CP\({}^{+}\) in the ECR task. We can see that the model performance demonstrates that modeling ECR as a discourse relation prediction task is feasible. By comparing the results of ECR-CP and ECR-CP\({}^{+}\), it can be observed that incorporating event features leads to improvements in precision and recall of the model, with recall showing a particularly significant improvement. ECR-CP\({}^{+}\) showed an improvement of 2.2% in precision, 7.9% in recall, and a 5.2% increase in F1 Score after incorporating the event features. This can be attributed to the fact that the determination of event co-reference heavily relies on the consistency of various entities involved in the events. Excessive occurrence of irrelevant content in the event description text may hinder the model's ability to capture crucial information. Consequently, without the explicit event feature \"Prompt\", the model tends to classify certain co-reference examples as non-coreferential. However, the introduction of event features significantly enhances the model's capability to identify positive instances. Furthermore, we conduct comparative experiments between several existing benchmark and state-of-the-art event-pair-based ECR methods and our models on the CCKS2021 dataset. The methods used in the comparative experiment are as follows:  
CNNs-SVM is a straightforward ECR method based on semantic similarity. Firstly, it applies convolution operations to the text matrix using multiple convolutional kernels of different sizes. Then, the outputs of each kernel are pooled and the concatenated output from different kernels forms the vector representation of the text. The textual vectors are taken as the input features of the support vector machine (SVM). Based on the training set, we can train the best SVM-based binary classification for the ECR task and determine the textual vectors of events. The classification threshold \(\sigma\) of the co-reference between event pairs is determined by computing the cosine similarity between their respective textual vectors. If the cosine similarity of event pairs in the testing set is greater than \(\sigma\), the event pairs are considered coreferent. Sentence-BERT is a network model proposed by [PERSON] et al. [36] for addressing semantic matching tasks. It is a widely adopted approach for computing semantic similarity. Sentence-BERT utilizes a Sentence-BERT model, similar to Siamese networks, to obtain vector representations \(u\) and \(v\) for two text segments. These vectors are then concatenated as \([u,v,|u-v|]\), and passed through fully connected layers. The SoftMax classifier is employed for text semantic matching, specifically determining if the events are coreferent. More details of Sentence-BERT can be learned according to [36]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **P (\%)** & **R (\%)** & **F1-Score (\%)** \\ \hline ECR-CP & 96.3 & 89.0 & 92.5 \\ ECR-CP\({}^{+}\) & 98.5 (2.2) & 96.9 (7.9) & 97.7 (+5.2) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The performance of ECR-CP and ECR-CP\({}^{+}\) on the test set. CorefNet is an ECR model based on a convolutional neural network with a multi-attention mechanism, proposed by [PERSON] et al. [20]. It leverages multiple layers of convolutional operations to extract text features, utilizes dot-product attention to discover shared features in two events, and incorporates a self-attention mechanism to capture important features within sentences. Additionally, it integrates semantic role features to improve the model's performance. The more details of CorefNet can be learned according to [20]. EPASE is an ECR model proposed by [PERSON] et al. [31], which utilizes Event-specific Paraphrases and Argument-aware Semantic Embeddings. The paraphrases' embedding module aims to identify different expressions denoting the same event across different sentences.