# Similarity Calculation via Passage-Level Event Connection Graph
## 6 Conclusions and Future Work  
Text similarity calculation is a fundamental task for many high-level artificial intelligence applications, such as text clustering, text summarization, and Q&A. Traditional similarity calculations are conducted in terms of either making two similar texts close in a high-dimensional space (supervised methods) or measuring the number of concurrent words shared by two texts (unsupervised methods). They ignore a fact that, in many scenarios, text is used to record events. Text similarity is mostly decided by whether two texts mention the same core event or not. This paper just proposes a novel text similarity calculation via constructing an event connection graph to disclose the core event mentioned in one text. To better model the relations among events, we tune the vectors of the triggers to detect related events and link them in the event connection graph. This approach can locate the core event more accurately. The nodes which can represent the core event are chosen and utilized to measure text similarity. Moreover, we adopt graph embedding to \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & \multicolumn{3}{c}{**English**} & \multicolumn{3}{c}{**Chinese**} & \multicolumn{1}{c}{**C-E**} & \multicolumn{1}{c}{**E-C**} \\ \cline{2-9} \multicolumn{2}{c}{} & **Para** & **Q\&Q** & **MA** & **Q\&Q** & **MA** & **Q\&Q** & **Q\&Q** \\ \hline \multirow{9}{*}{supervised} & TextCNN & 0.61 *** & 0.60 *** & 0.49 *** & 0.47 * & 0.51 * & 0.31 ** & 0.27 ** \\  & LSTM & 0.56 ** & 0.59 ** & 0.51 * & 0.54 ** & 0.52 ** & 0.23 * & 0.22 * \\  & LSTM+BIA & 0.54 ** & 0.53 ** & 0.47 * & 0.55 ** & 0.56 ** & 0.26 * & 0.24 * \\  & BERT-base & 0.57 *** & 0.55 ** & 0.53 ** & 0.49 *** & 0.51 ** & 0.48 ** & 0.41 ** \\  & Con-SIM & 0.52 *** & 0.53 *** & 0.51 * & 0.47 *** & 0.50 * & 0.44 ** & 0.38 ** \\  & RCMD & 0.53 *** & 0.56 *** & 0.54 * & 0.46 *** & 0.53 ** & 0.42 ** & 0.43 ** \\ \hline \multirow{3}{*}{unsupervised} & AVE & 0.58 * & 0.61 ** & 0.42 * & 0.63 ** & 0.49 * & 0.61 ** & 0.63 ** \\  & TR+AVE & 0.65 ** & 0.66 ** & 0.49 ** & 0.68 ** & 0.55 ** & 0.66 ** & 0.68 ** \\ \cline{1-1}  & TR+CON & 0.69 * & 0.71 *** & 0.53 ** & 0.66 ** & 0.53 ** & 0.71 *** & 0.66 ** \\ \cline{1-1}  & Ours & **0.82 *** & **0.86 *** & **0.68 *** & **0.89 *** & **0.67 *** & **0.86 *** & **0.89 *** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of all the algorithms in the transferring scenario (highest values in bold. ***, **, and * indicate three significant levels as 0.01, 0.05, and 0.1). tune the vectors of the chosen nodes to integrate the global information carried by the entire text into the chosen nodes. This way can further boost the performance of our calculation. Experimental results prove the high performance of our similarity calculation. Though our paper can combine the merits from supervised and unsupervised similarity calculations and can be applied in many text-related downstream applications which need text similarity as their component. Our calculation has time issue needed to be further solved. In particular, our calculation needs to form a passage-level event representation. This kind of operation needs extra time. Thus, though our calculation has higher accuracy, it is not fit to online applications, especially some time-insensitive applications. One issue needed to be mentioned is that, to link semantically similar triggers to let our event connection graph cover more relations among events, we need to predefine a threshold to decide whether two triggers are similar or not. As shown in the experiments, this parameter setting is not optimal for some corpus. It is chosen via balancing the results across all the testing corpora. Therefore, in the future work, we hope to set it dynamically. The other work we hope to carry out is to improve efficiency. The process of graph construction is time-consuming. We hope to construct some template graphs at advance.