Mining causal relations from maritime accident investigation reports

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Volume Title
School of Science | Master's thesis
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Date
2013
Major/Subject
Informaatiotekniikka
Mcode
T-61
Degree programme
Language
en
Pages
x + 58 s. + liitt. 6
Series
Abstract
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 thesis, 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 thesis 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 provides opportunity to improve safety in future. Two methods have been employed in this thesis to extract the causal relations. They are 1) Pattern classification method and 2) Connectives method. The earlier one uses na'ive 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 evidence, that text mining methods could be employed in extracting causal relations from marine accident
Description
Supervisor
Oja, Erkki
Thesis advisor
Lindh-Knuutila, Tiina
Hänninen, Maria
Keywords
pattern classification, connectives method, causal relations, SVM, naive Bayes, information extraction, MAIB
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