GlyReShot: A glyph-aware model with label refinement for few-shot Chinese agricultural named entity recognition.

Chinese agricultural named entity recognition (NER) has been studied with supervised learning for many years. However, considering the scarcity of public datasets in the agricultural domain, exploring this task in the few-shot scenario is more practical for real-world demands. In this paper, we propose a novel model named GlyReShot, integrating the knowledge of Chinese character glyph into few-shot NER models.

Improving Automated Essay Scoring by Prompt Prediction and Matching.

Automated essay scoring aims to evaluate the quality of an essay automatically. It is one of the main educational application in the field of natural language processing. Recently, Pre-training techniques have been used to improve performance on downstream tasks, and many studies have attempted to use pre-training and then fine-tuning mechanisms in an essay scoring system.

CharAs-CBert: Character Assist Construction-Bert Sentence Representation Improving Sentiment Classification.

In the process of semantic capture, traditional sentence representation methods tend to lose a lot of global and contextual semantics and ignore the internal structure information of words in sentences. To address these limitations, we propose a sentence representation method for character-assisted construction-Bert (CharAs-CBert) to improve the accuracy of sentiment text classification. First, based on the construction, a more effective construction vector is generated to distinguish the basic morphology of the sentence and reduce the ambiguity of the same word in different sentences.

Preparation process optimization of pig bone collagen peptide-calcium chelate using response surface methodology and its structural characterization and stability analysis.

In this study, alcalase and neutrase were used in combination to prepare collagen peptides with high calcium binding ability. The optimal conditions for the preparation of peptide-calcium chelate (mass ratio of peptide/calcium of 4.5:1 for 40 min at 50 °C and pH 9) were determined by response surface methodology (RSM), under which a calcium chelating rate of 78.