This paper presents a novel multi-strategy fusion neural network Named Entity Recognition (NER) method aimed at identifying entities in Process Specification Text (PST). To address the challenges of short sentence length, entity type error, and entity boundary error caused by limited context and semantic information in small sample data, the proposed method incorporates semantic enhancement (BM25, SBERT) and data augmentation (AUG) techniques. Additionally, a constraint matrix (Matrix) is designed to improve the consistency of the model and address the inconsistency caused by the pre-training model in the BERT-CRF model. The proposed method demonstrates significant improvements in both model consistency and F1 value. Specifically, the model consistency is improved from 90.71% to 94.51%, and the F1 value is increased from 76.90% to 77.21%. These results validate the effectiveness of the proposed approach for NER in PST.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.