# Classification and Causes Identification of Chinese Civil Aviation Incident Reports
## 4 Cause Extraction
### System Design  
Rule-based models and machine-learning algorithms are two major approaches to building a cause identification or classification system. Recently, machine-learning algorithms have been applied to text analysis on accident reports. Unfortunately, these methods have several limitations in this study. First, it is difficult to find the description of causes in reports, so the characteristics of the causes will be uncertain for these methods. In most \begin{table}
\begin{tabular}{l l l} \hline \hline
**Hazard** & **Code** & **Frequency of Occurrence** \\ \hline Equipment & EI & \\ Dynamical system malfunction & EI1 & 70 \\ Component failure & EI2 & 91 \\ Landing gear and tire malfunction & EI3 & 17 \\ Electric system and control system malfunction & EI4 & 53 \\ Autopilot disengaged and sensor fault & EI5 & 44 \\ Communication equipment disconnection & EI6 & 15 \\ Flaw in design & EI7 & 10 \\ Equipment breakdown in cabin & EI8 & 5 \\ Environment & E & \\ Bird strike & E1 & 411 \\ Slippery runway & E2 & 7 \\ Low visibility and bad weather & E3 & 159 \\ Wind shear & E4 & 117 \\ Discomfort of flight crew and passengers & E5 & 8 \\ Strike by other objects & E6 & 52 \\ Unusual terrain & E7 & 25 \\ Human & H & \\ Misjudgement and incorrect behaviour of crew & H1 & 280 \\ Deregulation of crew & H2 & 216 \\ Poor preparation and capacity of crew & H3 & 214 \\ Disorder of crew resource management & H4 & 159 \\ Distraction and negligence of crew & H5 & 231 \\ Maloperation of ground crew & H6 & 93 \\ Failure in communication with air traffic control (due to human error) & H7 & 88 \\ Organization & O & \\ Imperfect flight standards & O1 & 26 \\ Insufficient supervision of operational practices & O2 & 85 \\ Insufficient safety training of staff & O3 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Causes and occurrences. cases, the description of each cause appears in a sentence without a fixed location, and each report consists of dozens of sentences. Second, the identification effect of causes with low support is poor. A fairly high number of cases (e.g., 75 to 100) is the minimum needed to obtain a valid statistical model, even when learning shorter texts. However, several reasons, such as E8, C2, and C5, occur fewer than ten times in our dataset. Third, machine-learning algorithms cannot perform effective multilabel classification in our dataset. If each sample has one or more labels, 25 categories are too large a number for classifying 1775 samples. Fourth, though unsupervised machine-learning algorithms allow classification based on word distribution, they cannot meet the requirement of identifying specific causes. Therefore, a rule-based system seemed to be a better choice because of the following advantages. First, the precondition of the dataset scale is not specified if using rules. We can design specific rules for each cause, even without high support. Second, the upgrading and iteration for rules in a rule-based system can directly improve performance and can easily determine the content related to rules. In contrast, statistical models improve their performance by blind tuning the parameters, which indicates only a better result or a worse one, without knowing the details. Third, the rule-based system enables the intervention of human knowledge and intelligence. In a specific domain, professional information matters since it goes beyond the limits of available data. While statistical models can obtain broad but shallow features, a rule-based model provides considerable insight into the incident-related context. [PERSON] [11] used an unsupervised approach based on grammar rules, namely, chunking, to extract common causes of construction accidents. As noted in [5], such a system is not elegant but is effective. As mentioned above, the rule-based system is composed of cause-related keywords and rules. The rules, which can be regarded as grammar are established based on the keywords. Therefore, they would be updated at the same time.