“目前基于预训练语言模型的司法领域低资源法律文书命名实体识别研究主要面临两个问题:(1)在低资源语言中,如维吾尔语,法律文书相关的语料极其有限,这种语料资源稀缺限制了基于预训练语言模型的训练和性能。(2)法律文书中使用的专业术语不仅复杂且特定,新的法律术语和概念的出现使得现有的模型难以适应。针对上述问题,本文基于多语言预训练模型mBERT,通过领域词汇扩充及模型微调的方法,提升了模型在维吾尔语法律文书命名实体识别任务的性能。本文首先整理并构建维吾尔语司法领域专业词汇列表,并将其添加到mBERT模型的词汇表中。随后,在人工标注的维吾尔语法律文书命名实体数据集UgLaw-NERD上进行模型微调,验证了该方法的有效性。实验结果表明,相比于仅使用mBERT进行微调的基线模型,融合领域词汇扩充的模型在命名实体识别任务上F1得分提升至89.72%,较基线提高了7.39%。此外,本文还探讨了不同领域词汇扩充量对模型命名实体识别性能的影响,结果显示,领域词汇扩充增强了预训练模型在处理维吾尔语任务中的表现。这些结论为其他低资源语言在司法领域开展基于预训练模型的自然语言处理研究提供了有益的参考。”

“研究突发公共卫生事件国际舆情演变规律,对国际舆情资源进行应急管理和舆论疏导有重要借鉴价值。本文使用谷歌新闻数据库以各国针对COVID-19的报道为对象,构建国际舆情数据集。采用主题模型、图神经网络模型,结合时间、空间维度与舆情生命周期探究全球舆论主题-情感的演化态势,模型准确率为0.7973,F1值为0.7826,性能优于其他基线模型。研究发现,各国舆情呈现放射传播状态。国际媒体舆论的情感倾向和讨论主题存在正相关且随时间进行转变。”

“社交媒体是药物不良反应(ADR)检测的重要途径之一。本文提出一个基于社交媒体的药物不良反应检测模型DMFE,以全面捕捉患者对药物使用的反馈信息。与传统的文本检测相比,社交媒体数据中通常会有语法不规范与单词拼写错误的问题。本文提取出社交媒体数据的抽象语义表示(AMR)使用图注意力网络(GAT)学习抽象语义特征提高模型对语义信息的理解,使用字符级卷积神经网络(charCNN)捕获字符特征以减少单词拼写错误带来的影响。此外,本文使用提示学习的方法融入荍荥荤荄荒荁药物不良反应领域关键词进一步增强模型对领域知识的理解能力。经实验评估,本文模型DMFE在CADEC、TwiMed两个数据集上F1值与基线模型相比取得最优效果。”

“从文本中自动识别人类表型概念对疾病分析具有重大意义。现存本体驱动的表型概念识别方法主要利用本体中概念名和同义词信息,并未充分考虑本体丰富信息。针对此问题,本文提出一种基于本体信息增强的人类表型概念识别方法,利用先进大语言模型进行数据增强,并设计本体向量增强的深度学习模型来提升概念识别性能。在GSC+和ID-68两个数据集上进行实验,结果表明本文提出方法能够利用本体丰富信息有效提升基线模型性能,取得了先进结果。”

“The rapid development of artificial intelligence has led to an explosion of literature in the biomed-ical field, and Biomedical Named Entity Recognition (BioNER) can quickly and accurately iden-tify key information from unstructured text. This task has become an important topic to promotethe rapid development of intelligence in the biomedical field. However, in the Named EntityRecognition (NER) of the biomedical field, there are always some problems of unclear boundaryrecognition, the underutilization of hierarchical information in sentences and the scarcity of train-ing data resources. Based on this, this paper proposes a multi-task BioNER model based on dataaugmentation, using four data augmentation methods: Mention Replacement (MR), Label-wisetoken Replacement (LwTR), Shuffle Within Segments (SiS) and Synonym Replacement (SR)to increase the training data. The syntactic information is extracted by incorporating the inputsentence into the Graph Convolutional Network (GCN), and then the tag information encodedby BERT is interacted through a co-attention mechanism to obtain an interaction matrix. Subse-quently, NER is performed through boundary detection tasks and span classification tasks. Com-parative experiments with other methods are conducted on the BC5CDR and JNLPBA datasets,as well as the CCKS2017 dataset. The experimental results demonstrate the effectiveness of themodel proposed in this paper.”

“Multimodal Named Entity Recognition (MNER) is a challenging task in social mediadue to the combination of text and image features. Previous MNER work has focused onpredicting entity information after fusing visual and text features. However, pre-traininglanguage models have already acquired vast amounts of knowledge during their pre-training process. To leverage this knowledge, we propose a prompt network for MNERtasks (P-MNER).To minimize the noise generated by irrelevant areas in the image, wedesign a visual feature extraction model (FRR) based on FasterRCNN and ResNet, whichuses fine-grained visual features to assist MNER tasks. Moreover, we introduce a textcorrection fusion module (TCFM) into the model to address visual bias during modalfusion. We employ the idea of a residual network to modify the fused features using theoriginal text features. Our experiments on two benchmark datasets demonstrate that ourproposed model outperforms existing MNER methods. P-MNER’s ability to leveragepre-training knowledge from language models, incorporate fine-grained visual features,and correct for visual bias, makes it a promising approach for multimodal named entityrecognition in social media posts.”

“Zero-shot stance detection intends to detect previously unseen targets’ stances in the testingphase. However, achieving this goal can be difficult, as it requires minimizing the domain trans-fer between different targets, and improving the model’s inference and generalization abilities. To address this challenge, we propose an adversarial network with external knowledge (ANEK)model. Specifically, we adopt adversarial learning based on pre-trained models to learn transfer-able knowledge from the source targets, thereby enabling the model to generalize well to unseentargets. Additionally, we incorporate sentiment information and common sense knowledge intothe contextual representation to further enhance the model’s understanding. Experimental re-sults on several datasets reveal that our method achieves excellent performance, demonstratingits validity and feasibility.”