The traditional approach for text classification tasks commonly involves fine-tuning existing general-purpose or base models. However, when training data is extremely scarce, this approach may lead to overfitting or getting stuck in local optima. This paper investigates meta-learning-based methods for low-resource text classification, taking a diabetes dataset as an example, and proposes a meta-learning framework combined with data augmentation techniques. Firstly, we utilize an abstract summarization method to effectively augment the original dataset, alleviating potential issues caused by data imbalance while enhancing the diversity and generalization of the samples. Then, by employing a meta-learning algorithm, we achieve optimization and adjustment of the global initialization parameters. Subsequently, these parameters will guide the fine-tuning of the pre-trained BERT model to adapt to the diabetes text classification task. Experimental results show that our method significantly improves classification performance under low-resource conditions, providing new insights for handling low-resource text classification problems in similar domains.
The task of fact verification is to evaluate the truth of a claim-focused on plausible evidence, verifying the truth of a claim in public health is challenging because the need for reasoning across various retrievable pieces of evidence. Most fact-checking studies typically focus only on political claims, while few studies address fact verification on other topics. Therefore, this paper chooses to study fact verification in public health. To support this case study, this paper employs PUBHEALTH, a dataset containing claims related to public health. In this study, this paper presents an approach based on a semantic hierarchy. Different from most methods, which represent evidence sentences by concatenating or merging the characteristics of isolated evidence sentences, our method obtains the complex semantic hierarchy of evidence sentences through semantic analysis. Specifically, based on the XLNet model, the graph structure is initially utilized to redefine the relative distance of words. Then, a graph attention network and multi-layer graph convolutional network are used to propagate and aggregate information from neighboring nodes on the graph. Experiments on the PUBHEALTH dataset show that the proposed method achieves 76.2% in F1 score, which is substantially higher than the current highest score, indicating the effectiveness of the fact verification task based on semantic hierarchy.
KEYWORDS: Feature extraction, Data modeling, Machine learning, Transformers, Education and training, Deep learning, Associative arrays, Lithium, Performance modeling, Medical research
Electronic medical records are important information for medical intelligence, although there have been many related studies. However, based on the characteristics of the electronic medical record itself, for example, there is no clear boundary of participle and other problems, which causes difficulties to the research. Chinese, as a kind of hieroglyphic script, contains rich information in itself. There are various methods to use the splitting of Chinese characters as an enhanced data input model to improve the overall recognition effect; however, there are various forms of splitting of glyphs, and there are no papers to compare and contrast this information enhancement method. In this paper, firstly, the common ways of Chinese NER are introduced, and then they are analyzed through the technical point of view, followed by illustrating the effects of different splitting methods on the NER task, and comparing this data enhancement method through experiments.
To address the current problems of combining single domain-specific knowledge and poor fusion in suicidal ideation detection tasks, this paper proposes a Multi-Head Knowledge Attention Mechanism model that fuses domain knowledge (DK-MHKA) to fully integrate the suicide risk severity lexicon and the user's neurotic personality traits. The model involves integrating suicidal tendencies attributes into the semantic domain that encompasses the user's social media content, with the aim of enhancing the model's linguistic representations. Furthermore, the method employs a multi-head knowledge attention mechanism to effectively combine various sources of features, resulting in an enhanced predictive capability of the model. The experimental findings indicate that the suggested DK-MHKA model outperforms alternative baseline models in terms of forecasting precision. Additionally, the ablation experiments confirm the individual contributions of each module to the overall performance of the model.
Conversational humor often depends on context. Compared to one-liner humor, the task of conversational humor recognition is more complex and difficult. In addition, characters are one of the most important factors in dialogue, and most of the existing research on conversational humor recognition does not consider character information, resulting in poor results. Therefore, this paper proposes a conversational humor recognition model that combines character information and contextual feature. The main and supporting characters are set in a specific sitcom, and their gender is used as character attribute. RoBERTa, Bi-GRU, CNN and Attention are used to extract utterance feature at the word level and contextual feature at the sentence level to recognize one-liner humor and conversational humor in dialogue. This paper conducted experiments on CCL2020 Task 3 and achieved an F1-score of 53.7%, which is an improvement of 2.2% over the current best score, demonstrating the effectiveness of character information and contextual feature on the conversational humor recognition task.
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