ABSTRACT
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. Although the utilization of glyph has been proven successful in supervised models, two challenges still persist in the few-shot setting, i.e., how to obtain glyph representations and when to integrate them into the few-shot model. GlyReShot handles the two challenges by introducing a lightweight glyph representation obtaining module and a training-free label refinement strategy. Specifically, the glyph representations are generated based on the descriptive sentences by filling the predefined template. As most steps come before training, this module aligns well with the few-shot setting. Furthermore, by computing the confidence values for draft predictions, the refinement strategy selectively utilizes the glyph information only when the confidence values are relatively low, thus mitigating the influence of noise. Finally, we annotate a new agricultural NER dataset and the experimental results demonstrate effectiveness of GlyReShot for few-shot Chinese agricultural NER.
ABSTRACT
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. However, obtaining better features such as prompts by the pre-trained encoder is critical but not fully studied. In this paper, we create a prompt feature fusion method that is better suited for fine-tuning. Besides, we use multi-task learning by designing two auxiliary tasks, prompt prediction and prompt matching, to obtain better features. The experimental results show that both auxiliary tasks can improve model performance, and the combination of the two auxiliary tasks with the NEZHA pre-trained encoder produces the best results, with Quadratic Weighted Kappa improving 2.5% and Pearson's Correlation Coefficient improving 2% on average across all results on the HSK dataset.
ABSTRACT
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. At the same time, it aims to strengthen the representation of salient words and effectively capture contextual semantics. Second, character feature vectors are introduced to explore the internal structure information of sentences and improve the representation ability of local and global semantics. Then, to make the sentence representation have better stability and robustness, character information, word information, and construction vectors are combined and used together for sentence representation. Finally, the evaluation and verification are carried out on various open-source baseline data such as ACL-14 and SemEval 2014 to demonstrate the validity and reliability of sentence representation, namely, the F1 and ACC are 87.54% and 92.88% on ACL14, respectively.
Subject(s)
Chara , Language , Reproducibility of Results , Semantics , Sentiment AnalysisABSTRACT
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 40â¯min at 50⯰C and pH 9) were determined by response surface methodology (RSM), under which a calcium chelating rate of 78.38% was obtained. The results of Ultraviolet-Visible (UV-Vis), fluorescence and Fourier transform infrared (FT-IR) spectra synthetically indicated that calcium could be chelated by carboxyl oxygen and amino nitrogen atoms of collagen peptides, thus forming peptide-calcium chelate. The chelate was stable at various temperatures and pH values, and exhibited excellent stability in the gastrointestinal environment, which could promote calcium absorption in human gastrointestinal tract. Moreover, Caco-2 cell monolayer model was used to investigate the effect of peptide-calcium chelate on promoting calcium absorption. Results showed that peptide-calcium chelate could significantly improve calcium transport in Caco-2 cell monolayer and reverse the inhibition of calcium absorption by phosphate and phytate. The findings provide a scientific basis for developing new calcium supplements and the high-value utilization of pig bone.
