# LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event Extraction
## 3 Materials and Methods
### Corpus Annotation  
* _Data preprocessing_: the HPI text of Chinese EMRs records the whole process of patients from illness to hospitalization and treatment, including symptoms, disease development, diagnosis results, detailed process of examination and treatment, etc. Considering the varied writing habits of doctors, the expression of the same disease, operation, or drug name may differ. There are abbreviations and variants which bring difficulties to the follow-up work. Therefore, the data need to be preprocessed. For example, the sentence segmentation for long sentences containing multiple events, deleting sentences unrelated to medical events, unifying medical vocabulary such as disease, operation, drug, standardizing disordered punctuation;
* _Defining event types and element arguments_: I2B2 [13] held in 2012 defines a medical event as status, process, occurrence, and change, which are medically related. According to the definition of event extraction task in ACE and the definition of the medical event in I2B2, considering the content of the data set, the DTE of HPI are divided into six categories: admission, examination, test, treatment, operation, and diagnosis. The test events are subdivided into pathological test and immunohistochemistry, and the treatment events are subdivided into general treatment and chemotherapy. The event types and their event arguments are shown in Table 3;
* _Building a dictionary of candidate ETWs_: the ETW is the core word that indicates the occurrence of a certain event in the sentence, generally verb or gerund. The words with high frequency in each type of events are selected in this paper as the candidate ETWs for each type of event. As shown in Table 3, the dictionary is expanded by using the synonyms of the candidate ETWs in the synonym forest;
* _Defining entity types_: I2B2 and CCKS2017 ([[http://www.sigkg.cn/ccks2017/?page_id=51](http://www.sigkg.cn/ccks2017/?page_id=51)]([http://www.sigkg.cn/ccks2017/?page_id=51](http://www.sigkg.cn/ccks2017/?page_id=51)) (accessed on 18 January 2020)), respectively, divide medical named entities into three categories (medical problem, examination, treatment) and five categories (treatment, body part, symptom, examination, disease). Symptom entities are divided into body parts and symptom descriptions, and treatment entities into drug and operation in CCKS2018 ([[http://www.sigkg.cn/ccks2018/?page_id=16](http://www.sigkg.cn/ccks2018/?page_id=16)]([http://www.sigkg.cn/ccks2018/?page_id=16](http://www.sigkg.cn/ccks2018/?page_id=16)) (accessed on 18 January 2020)). Therefore, the entities in the data set are divided into three categories disease, symptom, and treatment. The symptom includes the body part and symptom description, and treatment includes drug, operation, and general treatment;
* _Corpus semi-automatic annotation_: each HPI text contains 30-40 entities; manual annotation is time-consuming and labor-consuming. Therefore, this paper annotates corpus by a semi-automatic method based on a medical dictionary. Firstly, entity dictionary is collected from medical dictionaries (e.g., Dingxiangyuan ([[https://portal.dxy.cn/](https://portal.dxy.cn/)]([https://portal.dxy.cn/](https://portal.dxy.cn/)) (accessed on 21 September 2019)), Sogou Medical ([[https://pinyin.sogou.com/dict/cate/index/132](https://pinyin.sogou.com/dict/cate/index/132)]([https://pinyin.sogou.com/dict/cate/index/132](https://pinyin.sogou.com/dict/cate/index/132)) (accessed on 1 September 2019)) and 39 Health Net ([[http://www.39.net/](http://www.39.net/)]([http://www.39.net/](http://www.39.net/)) (accessed on 18 September 2019)), then a reverse maximum matching algorithm is employed to tag the texts automatically. Secondly, the ETWs, EAs, and ETW types in texts are manually annotated, referring to the definition of the event type. Finally, two doctors are invited to check and correct the above annotation results.