# Short Text Event Coreference Resolution Based on Context Prediction
## 5 Experimental Results and Discussion
Meanwhile, the argument-aware semantic embedding module incorporates event arguments, such as subject and object, to differentiate between distinct event descriptions. More details of EPASE can be learned according to [31]. Lemma Heuristics (LH), proposed by [PERSON] et al. [37], is an ECR method that decomposes the ECR problem into two parts: (a) an effective heuristic approach for filtering a large number of non-coreferent mention pairs, and (b) a training method for balancing sets of coreferent and non-coreferent mention pairs. This significantly reduces computational requirements, enabling the algorithm to learn coreference beyond surface matching and effectively improve the model training results. The more details of LH can be learned according to [37]. Table 2 presents the experimental results of various methods on the CCKS 2021 dataset for ECR. It is evident that ECR-CP\({}^{+}\), which utilizes event feature fusion encoding, achieves optimal performance in terms of precision, recall, and F1 Score, achieving scores of 98.5%, 96.9%, and 97.7% respectively. Compared to the best-performance of existing methods in three evaluation metrics, ECR-CP\({}^{+}\) shows improvements of 1.0%, 0.4%, and 2.9% in these respective metrics. However, both the CNNs-SVM model, which employs traditional CNN for text feature extraction, and CorefNet demonstrate subpar performance. This could be attributed to the difficulty in establishing distinct classification boundaries among text vectors, resulting in the suboptimal effectiveness of using machine learning to determine similarity thresholds. Although CorefNet benefits from multiple attention mechanisms and incorporates semantic role features, its performance falls short of other BERT-based models due to limitations in traditional neural network feature extraction capabilities. Furthermore, Sentence-BERT, EPASE, and LH exhibit better performance than ECR-CP, but fall short compared to ECR-CP\({}^{[+]}\) that incorporates event features. This suggests that modeling the ECR task as a sentence-pair prediction is effective, and the inclusion of event features significantly enhances the model's recall for coreferent events. Based on a comprehensive analysis, the enhancement in the performance of ECR-CP\({}^{+}\) can be primarily attributed to three factors:  
1. By modeling ECR as a sentence-pair prediction task, a single model is capable of extracting features from both event texts, which facilitates information interaction and enables the language model to more effectively identify the shared characteristics and distinctions among event pairs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **P (\%)** & **R (\%)** & **F1-Score (\%)** \\ \hline CNNs-SVM & 72.3 & 71.7 & 72.0 \\ Sentence-BERT & 97.1 & 91.2 & 94.1 \\ CorefNet & 92.5 & 89.1 & 90.8 \\ EPASE & 97.5 & 92.3 & 94.8 \\ LH & 90.5 & 96.5 & 93.4 \\ ECR-CP (Ours) & 96.3 & 89.0 & 92.5 \\ ECR-CP\({}^{+}\) (Ours) & **98.5** & **96.9** & **97.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The experimental results of various methods for ECR based on the CCKS 2021 dataset. The best results are indicated in bold and the second ones are highlighted with underlines. Utilizing pre-trained models for feature extraction offers enhanced semantic capturing capabilities in contrast to traditional CNNs or custom-designed convolutional neural networks. Moreover, with the inclusion of the multi-head attention mechanism, it assigns greater weights to words that exert substantial influence on the automatic coreference outcomes. 3. The inclusion of event features, encompassing event type, trigger words, and arguments, to a certain extent, reflects the coreferential nature of events, particularly when dealing with semantically intricate event descriptions. Introducing event features notably improves the model's discernment of positive instances.