  Text mining is a process of extracting information of interest from text.
Such a method includes techniques from various areas such as Information
Retrieval (IR), Natural Language Processing (NLP), and Information Extraction
(IE). In this study, text mining methods are applied to extract causal
relations from maritime accident investigation reports collected from the
Marine Accident Investigation Branch (MAIB). These causal relations provide
information on various mechanisms behind accidents, including human and
organizational factors relating to the accident. The objective of this study is
to facilitate the analysis of the maritime accident investigation reports, by
means of extracting contributory causes with more feasibility. A careful
investigation of contributory causes from the reports provide opportunity to
improve safety in future.
  Two methods have been employed in this study to extract the causal relations.
They are 1) Pattern classification method and 2) Connectives method. The
earlier one uses naive Bayes and Support Vector Machines (SVM) as classifiers.
The latter simply searches for the words connecting cause and effect in
sentences.
  The causal patterns extracted using these two methods are compared to the
manual (human expert) extraction. The pattern classification method showed a
fair and sensible performance with F-measure(average) = 65% when compared to
connectives method with F-measure(average) = 58%. This study is an evidence,
that text mining methods could be employed in extracting causal relations from
marine accident investigation reports.
