关键词: 煤矿机电设备, 三元组抽取, 提示学习, 迭代式对抗训练, 自构语料库

Abstract:

To address the challenges of scarce domain-specific corpora， insufficient feature mining of relation types， and the presence of overlapping triplets in texts for electromechanical equipment domain， a triplet extraction method TBPA （Triplet extraction Based on Prompt and Antagonistic training） based on prompt learning with prior knowledge through iterative adversarial training was proposed. Firstly， the BERT （Bidirectional Encoder Representations from Transformers） model was fine-tuned on a self-constructed corpus to obtain feature vectors for input text. Then， an iterative adversarial training using the Projection Gradient Descent （PGD） method was conducted at the embedding layer to enhance the model’s resistance to perturbed samples and generalization ability to real samples. Furthermore， a single-layer head-tail pointer network was used to identify the head entity， and domain-specific prior features corresponding to the head entity were obtained by incorporating the word vectors with the prompt vectors predicted by the prompt learning templates. Finally， within a hierarchical annotation framework， another single-layer head-tail pointer network was employed to sequentially identify the tail entities associated with predefined relation types. In comparison with the baseline model CasRel， TBPA achieves improvements of 3.10， 6.12 and 4.88 percentage points in precision， recall， and F1 score， respectively. Experimental results demonstrate its advantages in triplet extraction tasks within the domain of mine electromechanical equipment.

Key words: mine electromechanical equipment, triplet extraction, prompt learning, iterative adversarial training, self- constructed corpora