# A Named Entity and Relationship Extraction Method from Trouble-Shooting Documents in Korean
###### Abstract  
In enterprises operating large-scale equipment, such as plants, maintenance workers must quickly and accurately find and understand the information in the equipment maintenance documents to perform maintenance tasks effectively. If the equipment maintenance documents include sentences with semantically ambiguous expressions, it will interfere with the maintenance knowledge search, and it may affect the maintenance performance of engineers. In order to solve these problems, text-based research of maintenance documents have been done to extract the key information or knowledge from these documents. Previous studies focused on finding the technical terminologies or calculating the similarity of documents using named entity recognition approaches. This paper proposes a method to extract knowledge of not only the technical terminologies but also their relations. The proposed method uses a rule-based approach that can be applied to the results of a named entity recognition approach and a dependency parsing approach. The named entity recognition approach found technical terms and the dependency parsing approach provided sentence structure information, so that the proposed method showed that a set of rules can extract maintenance knowledge, including entities and their relations. Trouble-shooting documents in the field were used as an experiment to demonstrate the effectiveness of the proposed method, and the experiment showed the possibility of practical use of the proposed method. dependency parsing; equipment maintenance documents; named entity recognition +
Footnote †: journal: Medical Imaging  
1
Footnote 1: email: [EMAIL_ADDRESS]  
Footnote †: journal: Medical Imaging  
of equipment could be found by automatically searching texts in documents with natural language processing. Some prior efforts have been made to extract maintenance knowledge from documents in order to efficiently manage equipment-related documents. A framework [1] to extract failure knowledge from failure reports has been proposed. The framework extracts failure knowledge by calculating the similarity of failure sentences, which are represented in normalized expressions. There were also two studies to extract maintenance knowledge from text documents in the engineering domain. The first of these studies [2] extracted ship failure knowledge from unstructured text data by using a named entity recognition (NER) approach. The second study [3] also implemented an NER approach to extract failure knowledge from maintenance logs of an air compressor. Knowledge extraction from documents involves an understanding of the semantic relations between items. Relation extraction from text plays a crucial role in natural language processing applications, including engineering-domain knowledge discovery. Traditionally, extracting the relations among entities in documents has been investigated as two main tasks: NER and relation extraction. This approach involves building two distinct models for entity recognition and relation extraction, and then optimizing them jointly by sharing key parameters [4; 5; 6; 7]. According to previous research, the performance of entity recognition is generally good, but relation extraction tasks remain challenging. The dependency parsing approach examines word dependencies to discover a sentence's grammatical structure, and it delivers substantial structural information that has been shown to be useful for extracting relations among entities in documents. Many dependency parsing models have been developed and implemented in various sectors, but they have not been applied to engineering documents. Developing a dependency parser for maintenance documents in a short period of time is hard, and it is also not easy to modify and utilize existing dependency parsers for maintenance documents. Existing approaches [1; 2; 3] to extract knowledge from maintenance documents have focused on finding sentences by calculating the similarity of sentences or recognized named entities.