Research on Entity Recognition in Aerospace Engine Fields Based on Conditional Random Fields

Abstract

With Entity recognition is an important basic tool for many natural language processing tasks such as information extraction, question answering systems, and syntactic analysis. Entity recognition is divided into general field and specific field. There were different recognition methods for different fields. At present, one of the more common entity recognition technologies is a rule-based method, and the other is a statistical method. Due to data sets scarcity in the aerospace engine field, there is a lack of corresponding entity recognition research in the aerospace engine field. This paper proposes a statistical-based entity recognition method to identify entities in a specific field based on conditional random fields. This method constructs a training set in the aerospace engine field through manual annotation, extracts entities in the aerospace engine field, uses words, word frequency, and part-of-speech features respectively, and conducts experiments. This method can more accurately identify entities in the aerospace engine field, and finally make the accuracy rate, Recall rate and F value reach 92.7%, 87.1%, 89.8%.