# Similarity Calculation via Passage-Level Event Connection Graph
## 4 Two Improvements Made on Our Event Connection Graph  
### Tuning Trigger Words  
The relations among events are mainly caused by trigger words. We should provide a way to find semantically similar triggers and link them in the event connection graph to involve more relations among events. It has been counted that about ninety percent of trigger words are nouns and verbs (or noun and verb phrases) [44; 45]. The pre-trained word embedding injects semantics in vector representation and obtains this representation counting on whether two words own the similar contexts or not. However, in event related tasks, we cannot merely depend on pre-trained word embedding to reveal semantic similarity between triggers. Trigger and its arguments have some commonly used collocations, e.g., \"kick football\" and \"play basketball\". That causes two trigger words which have high semantic similarity may have different contexts. Figure 3: PageRank values of the nodes in the event connection graph. To find semantically similar trigger words, we can borrow the help from some synonym dictionaries, like VerbNet and WordNet, two manually formed synonym dictionaries. These two dictionaries put semantically similar words in one synset. The synsets are organized in hierarchy. Unfortunately, dictionary cannot cover all the possible semantically similar trigger pairs. Anyway, it is time-consuming and laborious to manually construct such kind of dictionary. Therefore, we should provide a way to find semantically similar triggers independent of manual dictionary. In this paper, we try to fine-tune the vector representations of the trigger words to let semantically similar triggers own close vector representations. Two triggers whose cosine similarity is beyond the threshold (0.8) are connected through an arc to involve more rational relations among events. Figure 4 shows the event connection graph after connecting similar triggers, i.e., visit and communicate, and report and communicate. Regarding the threshold (0.8), it is set based on sufficient experimental results shown in the experimental section. GloVe [10] trained on wiki data is used as the basic trigger embedding. As shown in Figure 4, with the inserted arc, dotted line with green color, the node of the largest value changes to \"communicate\". That indicates, after involving novel relation, the core event can be revealed more correctly. Let \(B_{c}\) denote the set including the synonymous trigger pairs sampled from VerbNet and WordNet. We tune the vector representations of the triggers according to the following formulas:  
\[min\ O(B_{c})=min(O_{c}(B_{c})+R(B_{c})) \tag{2}\]  
\[O_{c}(B_{c})=\sum_{(x_{l},x_{r})\in B_{c}}[\tau(att+x_{l}l_{l}-x_{l}x_{r})+ \tau(att+x_{r}t_{r}-x_{l}x_{r})] \tag{3}\]  
\[R(B_{c})=\sum_{x_{l}\in B_{c}}\lambda\ \parallel x_{i}(int)-x_{i}\parallel 2 \tag{4}\]  
where \((x_{l},x_{r})\) denotes a synonymous word pair in \(B_{c}\). \(t_{l}\) is one word, randomly sampled from the synset which \(x_{l}\) and \(x_{r}\) are not in. So is to \(t_{r}\). \(att\) denotes the predefined deviation between the semantically similar word pair and the dissimilar one. It is set to 0.6. \(\tau\) denotes max margin loss, noted as \(max\ \tau(0,x)\). \(x_{i}(int)\) denotes the pre-trained GloVe vector. \(\lambda\) is a  
Figure 4: The event connection graph by linking semantically similar triggers. predefined regression parameter and is set to 0.0006. The predefined parameters are set according to [45]. As shown in Equation (2), the tuning formula has two parts. The former part (noted as \(O_{c}(B_{c})\)) refers to Equation (3), which makes similar triggers own similar vector representations. The latter part (noted as \(R(B_{c})\)) refers to Equation (4), which keeps the tuned vectors not far away from their pre-trained results. Since the pre-trained vectors are acquired from a large-scale corpus, we certainly do not want the tuned vectors to deviate from the pre-trained ones.