Named Entity Recognition task is one of the core tasks of information extraction. Word ambiguity and word abbreviation are important reasons for the low recognition rate of named entities. In this paper, we propose a novel named entity recognition model WCL-BBCD (Word Contrastive Learning with BERT-BiLSTM-CRF-DBpedia), which incorporates the idea of contrastive learning. The model first trains the sentence pairs in the text, calculate similarity between sentence pairs, and fine-tunes BERT used for the named entity recognition task according to the similarity, so as to alleviate word ambiguity. Then, the fine-tuned BERT is combined with BiLSTM-CRF to perform the named entity recognition task. Finally, the recognition results are corrected in combination with prior knowledge such as knowledge graphs, so as to alleviate the low-recognition-rate problem caused by word abbreviations. The results of experimentals conducted on the CoNLL-2003 English dataset and OntoNotes V5 English dataset show that our model outperforms other similar models on.
Optical computing systems provide high-speed and low-energy data processing but face deficiencies in computationally demanding training and simulation-to-reality gaps. We propose a gradient-based model-free optimization (G-MFO) method based on a Monte Carlo gradient estimation algorithm for computationally efficient in situ training of optical computing systems. This approach treats an optical computing system as a black box and back-propagates the loss directly to the optical computing weights' probability distributions, circumventing the need for a computationally heavy and biased system simulation. Our experiments on diffractive optical computing systems show that G-MFO outperforms hybrid training on the MNIST and FMNIST datasets. Furthermore, we demonstrate image-free and high-speed classification of cells from their marker-free phase maps. Our method's model-free and high-performance nature, combined with its low demand for computational resources, paves the way for accelerating the transition of optical computing from laboratory demonstrations to practical, real-world applications.