Abstract: Process text entity recognition aims to recognize entities such as parts,materials,attributes and attribute values from texts generated or associated with the manufacturing process of products.Recently,in most domain-specific entity recognition tasks,such as process domain,prior knowledge in the form of dictionaries or rules is used to adjust neural network model results or generate pre-recognized features to incorporate into the model.However,these methods do not realize the integration of domain entity recognition knowledge and neural network models.Furthermore,the addition of domain knowledge does not reduce the training cost of the model and still need a large amount of labeled data.To address these challenges,this paper proposes a bidirectional neural probabilistic transducer(Bi-NPT) for process text entity recognition.This approach models the domain-specific prior knowledge for process text entity recognition as regular rules,and then converts these rules into a parameterized probabilistic finite state transducer.This method makes the model carry entity recognition prior knowledge before training,while being traina-ble.The model acquires the ability to recognize entities not covered by the regular rules by training on labeled data.Experimental results demonstrate that the proposed Bi-NPT performs comparably to regular rule-based entity recognition without training,suggesting that the untrained initial model already has possess entity recognition knowledge.Additionally,Bi-NPT outperforms other methods such as PER,Template-based BART,NNShot in few-shot and BiLSTM,TENER in rich-resource scenarios.

Key words: Process text, Entity recognition, Regular rules, Probabilistic finite state transducer