# LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event Extraction
## 1 Introduction  
Information extraction is a critical task in natural language processing, which aims at extracted structured information from unstructured or semi-structured texts. Medical research, such as disease source [1, 2], disease prediction [3], and clinical decision [4, 5], needs to extract structured information from underlying medical corpus. This kind of effective extraction and learning for knowledge has become a significant task. Traditionally, the medical knowledge graph (KG) takes entities as nodes, edges as relations to describe medical concepts. However, in addition to the involved medical concepts, the rich and closely related diagnosis and treatment events (DTE) in electronic medical records (EMRs) also contain dynamic knowledge and disease development logic, which cannot be captured by a fine-grained conceptual structure. Therefore, it is necessary to study the extraction method of DTE [6, 7]. At present, automatic content extraction (ACE) [8] is the most famous event extraction conference. It divides event extraction (EE) into two subtasks: trigger word detection and event argument recognition. The event trigger word (ETW) is the word that can indicate the event type in the sentence, and the event argument (EA) is the participant that describes the event. For example, the sentence \"On 12 August 2015, the patient underwent radical resection of rectal cancer in our hospital due to rectal cancer\" is an operation event, \"underwent\" is an ETW, and the EAs include \"12 August 2015\", \"rectal cancer\", \"radical resection of rectal cancer\". Early research on medical DTE extraction mostly utilizes pattern matching [9, 10]. Recent studies have shown that the sequence annotation methods based on the deep neural network have positive effects on the medical field [11, 12]. These methods haveincorporated the context of the entities in the form of distributed word representation and illustrated the importance of domain knowledge in clinical medical event extraction. However, the basic-feature based EE methods merely adopt pattern matching or sequence annotation, which have limitations in fully mining the important but implied semantic dependency information contained in and between sentences. As shown in Table 1, both example 1 and example 2 are operation events. The ETWs and EAs contained in this type of event sentence have a certain regularity, such as that the ETWs are words of \"underwent\". EAs often contain entity words such as disease and operation name, while example 3 of pathological examination usually takes a long sentence-level test result as one of the EAs, and it is mostly located after the operation event sentence. In this paper, an elegant framework is proposed to recognize the complex ETWs and EAs in the rich DTEs in the history of present illness (HPI) texts of Chinese EMRs. The key idea of our work is to introduce different feature information to train the ETW classifier according to the distribution of entities and events at different corpus levels, and adopt different modules to realize the recognition of EAs with different granularity. Specifically, the contribution of this paper is three-fold:  
1. Filling up the gap in the current representation framework and annotated corpus of HPI event in Chinese EMRs based on the combined source from the ACE task definition and Informatics for Integrating Biology and the Bedside (I2B2) [13], through adopting the reverse maximum matching algorithm performing semi-automatically labeling of the PME corpus of Chinese EMRs. 2. The proposed Chinese HPI event extraction method which fusing long short-level semantic dependency (LSLSD) of corpus not only highlights the semantic dependence between the components in sentences, but also emphasizes the semantic dependence between sentences. Meanwhile, it distinguishes the granularity of EAs at the short word-level and long sentence-level can better capture the semantic information of the corpus and enhance the generalization ability of the model. 3.