Text Mining for Causes of Ship Accidents Based on PMI and BTM

The paper proposes a method of semantic mining for ship accident investigation reports from words and topics to automatically extract knowledge of water traffic safety from massive ship accident investigation reports. Moreover, 100 investigation reports on the self-sinking accidents of ships are used as corpus for specific excavations. At the word level, the PMI algorithm is used to mine frequent co-occurrence word patterns from the texts describing the causes of the accidents, and relationships between accident-causing factors are revealed through the co-occurrence of text feature words. At the topic level, the BTM algorithm is used to model the texts describing the causes of the accidents, and the modeling results are evaluated by topic log-likelihood and coherence. The feature words representing the causes of foundering accidents are clustered through topic modeling, and the occurrence probability of each cause is preliminarily quantified according to the distribution of topics in the corpus. According to the results on the predictive ability of the topic model using 500 new data sets, the topic model can recognize 100% of the domain-independent words and automatically ignore them. For 85.6% of the words in the corpus, the topic model can attribute them to a certain topic representing a specific cause. For about 14.4% of the words, the topic boundary is not obvious, so it is not easy to attribute them with a high probability.