Abstract
During the activity in the Failure Analysis (FA) laboratory, all corresponding findings and conclusions are included in a series of documents known as the FA reports. They shall, in the first place, inform the requestor about the analysis results. But additionally, they shall provide information to solve similar cases. Therefore, these documents play a key role in preserving the knowledge acquired by the engineers as they become available for consultation during future works. The different information systems in FA consist of databases, file shares, wikis, or other human-readable forms. However, the heterogeneity of these databases and the large number of independent documents make it inefficient for manual consultation. In this context, this paper proposes an application of Natural Language Processing (NLP) known as Named Entity Recognition (NER), consisting of an AI-based detection of key concepts in textual data in the form of annotations. These annotations can then be used to boost search systems or other AI models.
