# LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event Extraction
## 3 Materials and Methods
### Event Trigger Word Detection Module  
Event trigger word detection is a vital step in the event extraction task. Its result directly affects the accuracy of event extraction. In this paper, ETW detection is regarded as a multi-classification task. By analyzing the long short-level semantic dependence intra-event sentence and inter sentences in HPI texts, extended features are proposed to support the training of classifiers, and to obtain more accurate ETW detection results. Firstly, the words existing in the sentence contained in the candidate ETW dictionary are condemned as the ETW of the sentence. If the words are not found, the ETW will be replaced by words with higher similarity in the sentence and the candidate ETW dictionary. Then, several types of features commonly utilized in ETW detection are selected as the basic features, as shown in Table 4 (1-4). For details, please refer to paper [32]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Types** & **Number** & **Symbols** & **Names** & **Describes** \\ \hline \multirow{6}{*}{Basic feature} & \multirow{2}{*}{1} & \(f_{1}\) & Trigger word & Word vector by LTP 1 word segmentation tool \\ \cline{3-5}  & & 2 & \(f_{2}\) & Part-of-speech & Part-of-speech tagged by LTP \\ \cline{3-5}  & & 3 & \(f_{3}\) & context & Word and part-of-speech in window of length 3 \\ \cline{3-5}  & & 4 & \(f_{4}\) & dictionary & Event types corresponding to candidate ETW dictionary \\ \hline \multirow{6}{*}{Extended feature} & \multirow{2}{*}{Intra sentence} & \multirow{2}{*}{5} & \(f_{5}\) & Entity types & Distribution of entities in different types of event sentences \\ \cline{3-5}  & & & & \#2 different entity types & Number of entities in different types of event sentences \\ \cline{1-  
Based on the basic features suitable for general texts, extended features are designed for the entity distribution information in medical event sentences by analyzing the linguistic characteristic of Chinese HPI event sentences, as shown in Table 4 (5,6). Where feature \(f_{5}\) and \(f_{6}\), respectively, refer to the distribution and number of entities in different types of event sentences. These two extended features of short-level semantic dependency are proposed because each type of event sentence does not cover all six types of entities, and the number of entities is not the same; these distribution regulars play an important role in the classification of ETWs. In particular, there is a correlation between the events because HPI text describes the occurrence and evolution of the patient's disease. For example, the admission event does not occur after the operation event, and the pathological examination event does not occur before the operation event. Therefore, inspired by [24; 25], the extended feature of long-level semantic dependence refers to the consistency between event sentences in the HPI text is proposed named document consistency feature, as shown in Table 4 (7). Taking the operation event as an example, Figure 2 statistics the distribution of different event types before and after the operation event in the HPI texts. It can be seen that the probability of admission, diagnosis, and examination event occurring is higher before the operation event, but the probability of pathological test, chemotherapy, and general treatment event occurring is higher after the operation event. Based on the above semantic features, for a given HPI text \(R,R=\{S_{1},S_{2},\cdots,S_{n}\}\), where \(S_{n}=\{W_{n1},W_{n2},\cdots,W_{ni}\}\) is the \(n\)th event sentence and \(W_{ni}\) is the word in the sentence, where \(i\) express the sentence length (total number of words). The goal of ETW detection task is to obtain the classification \(P(T|S_{n})\) of ETW in the event sentence to determine the event type.