# Similarity Calculation via Passage-Level Event Connection Graph
## 2 Related Work
An iterative process is adopted to take the output cases as training data in turn [30]. This kind of calculations suffers from cold-starting issue, which needs to set initial similarity values beforehand, and the final results drop a lot on the inappropriate initialization. Table 1 just summarizes the difference between supervised text similarity calculation and unsupervised text similarity calculation. As indicated by the pervious table, it can be observed that these two kinds of similarity calculations both have corresponding merits and drawbacks. Supervised methods have higher performances due to its importing of training data. However, using training data is hard to alter domain. This situation causes that the performances of supervised methods drop sharply when domain changes. On the contrary, unsupervised methods have lower performances, while their performances do not drop along with domain alteration. In this situation, we try to propose an unsupervised similarity calculation to combine both merits of supervised and unsupervised methods. Features taken by previous calculations are words or word spans which contribute mostly to score functions (applied in supervised ones) or own some prominent distribution compared with other features (applied in unsupervised ones). Though among supervised calculations, some algorithms may learn a semantic embedding on word level or text level in terms of training data to help model the semantics in input text [31; 32; 33]. They all ignore a fact that most of web texts are used to record events. One text should tell one core event. The other mentioned events either help explain the core event or provide some details of the core event (such as time, place, or related events). In fact, the core event mostly decides the similarity between two texts. In other words, if one event is stressed by two texts meanwhile, these two texts are similar at a high possibility. Thus, the task of calculating text similarity can be fulfilled by comparing the discrepancy of the core events, respectively, extracted from two texts. The core event represents the main content of one text. It should be extracted from a passage-level viewpoint. Event extraction and representation have been studied during a long time. As the most famous event extraction tasks, MUC (Message Understanding Conference) [34] and ACE (Automatic Content Extraction) [35] have been held for about 30 years. The definition of event in MUC and ACE is sentence-level with trigger as key element and arguments as supplementary details. Traditional event extraction tasks assume that an event can be fully expressed by a single sentence. It can be extracted without taking other sentences \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature/Method** & **Supervised** & **Unsupervised** \\ \hline Training data & Results derived from training data & Free from training data \\ Performance & Higher performance & Lower performance \\ W/O semantics & Semantic embedding via representation & Lack of understanding semantics \\ Domain transfer & Hard & Easy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of supervised and unsupervised similarity calculation. into consideration. Since an event can be formatted as trigger and arguments, traditional sentence-level event extraction methods can be separated into two successive steps. The former step is called event detection (or trigger extraction), which aims to detect events and classify event type. The latter step is called argument extraction, which aims to acquire the arguments related to the trigger, such as time, location, subject, and object, etc. The algorithms designed for sentence-level event extraction are not appropriate to extract passage-level events, since they aim to learn a better representation for single sentences [36; 37] and not to model the relations among events across sentences. As told before, traditional event extraction methods treat sentences independently and extract events from a single sentence.