Nobuyuki Ohmori and Tatsunori Mori
Product development, tangibility
In product development, because product reliability requirements are often not fully satisfied, the prevention of product failure has become a matter of major concern. In order to support the development of reliable products, we propose information extraction methods that extract causal information from product failure documents. These methods use a binary classifier that is trained on the basis of lexico-syntactic patterns and a thesaurus, and we implement the binary classifier by using a machine learning system. In order to improve the accuracy of extraction, we introduce the notion of “tangible words.” The experimental results show that the proposed methods offer improved precision over a previous method. These methods help to prevent product failure by using information about failure and the causes of the failure that are related to products under development.
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