网络词汇研究综述

本文综述网络语言的研究现状,分别论述网络词汇的收集整理、性质、类型、来源、词汇义、规范问题的研究现状,并提出对未来网络语言研究的展望。     

黑龙江省边远地区英语基础教育中多媒体的运用及思考

随着经济社会的发展,英语教学对学生的综合素质提出了越来越高的要求,而多媒体的运用为教育教学活动提供了内容丰富的教学资源。然而,边远地区的多媒体教学存在思想落后、资金匮乏、技术薄弱等问题。本文着重讨论多媒体教学在边远地区英语基础教育中运用所产生的积极教学效果及其存在的一系列问题。     

焦虑对初中生英语听力的影响及对英语教学的启示

情感因素中的焦虑与二语习得之间存在着一定的相关性,许多外语专家认为焦虑会给外语学习带来负面影响。因此,降低学生的学习焦虑可以提高学生的学习效果和成绩。在本文中,笔者从揭示焦虑对初中生英语听力效果与成绩的影响入手,通过对这些影响进行分析,笔者得出一些提高学生听力能力的教学启示。     

情报检索语言的词汇控制问题

本文在情报检索语言和自然语言原理、交流方式等简要比较的基础上,对情报检索中词汇控制的必要性,词汇控制的内容,原则以及词汇控制对检索效率的影响,进行了探讨。     

图书馆态势语的隐性知识

本文从态势语对图书馆读者服务工作的影响层面切入,解读了什么是态势语和隐性知识,并详细介绍了在读者服务工作中应如何运用态势语和提高运用的途径。     

A Dual-Pointer guided transition system for end-to-end structured sentiment analysis with global graph reasoning

Structured sentiment analysis is a newly proposed task, which aims to summarize the overall sentiment and opinion status on given texts, i.e., the opinion expression, the sentiment polarity of the opinion, the holder of the opinion, and the target the opinion towards. In this work, we investigate a transition-based model for end-to-end structured sentiment analysis task. We design a transition architecture which supports the recognition of all the possible opinion quadruples in one shot. Based on the transition backbone, we then propose a Dual-Pointer module for more accurate term boundary detection. Besides, we further introduce a global graph reasoning mechanism, which helps to learn the global-level interactions between the overlapped quadruples. The high-order features are navigated into the transition system to enhance the final predictions. Extensive experimental results on five benchmarks demonstrate both the prominent efficacy and efficiency of our system. Our model outperforms all baselines in terms of all metrics, especially achieving a 10.5% point gain over the current best-performing system only detecting the holder-target-opinion triplets. Further analyses reveal that our framework is also effective in solving the overlapping structure and long-range dependency issues.     

Fake news detection via knowledgeable prompt learning

The spread of fake news has become a significant social problem, drawing great concern for fake news detection (FND). Pretrained language models (PLMs), such as BERT and RoBERTa can benefit this task much, leading to state-of-the-art performance. The common paradigm of utilizing these PLMs is fine-tuning, in which a linear classification layer is built upon the well-initialized PLM network, resulting in an FND mode, and then the full model is tuned on a training corpus. Although great successes have been achieved, this paradigm still involves a significant gap between the language model pretraining and target task fine-tuning processes. Fortunately, prompt learning, a new alternative to PLM exploration, can handle the issue naturally, showing the potential for further performance improvements. To this end, we propose knowledgeable prompt learning (KPL) for this task. First, we apply prompt learning to FND, through designing one sophisticated prompt template and the corresponding verbal words carefully for the task. Second, we incorporate external knowledge into the prompt representation, making the representation more expressive to predict the verbal words. Experimental results on two benchmark datasets demonstrate that prompt learning is better than the baseline fine-tuning PLM utilization for FND and can outperform all previous representative methods. Our final knowledgeable model (i.e, KPL) can provide further improvements. In particular, it achieves an average increase of 3.28% in F1 score under low-resource conditions compared with fine-tuning.     

AugPrompt: Knowledgeable augmented-trigger prompt for few-shot event classification

Few-Shot Event Classification (FSEC) aims at assigning event labels to unlabeled sentences when limited annotated samples are available. Existing works mainly focus on using meta-learning to overcome the low-resource problem that still requires abundant held-out classes for model learning and selection. Thus we propose to deal with the low-resource problem by utilizing prompts. Further, existing methods suffer from severe trigger biases that may result in ignorance of the context. That is, the correct classifications are gained by looking at only the triggers, which hurts the model’s generalization ability. Thus, we propose a knowledgeable augmented-trigger prompt FSEC framework (AugPrompt), which can overcome the bias issues and alleviates the classification bottleneck brought by insufficient data. In detail, we first design an External Knowledge Injection (EKI) module to incorporate an external knowledge base (Related Words) for trigger augmentation. Then, we propose an Event Prompt Generation (EPG) module to generate appropriate discrete prompts for initializing the continuous prompts. After that, we propose an Event Prompt Tuning (EPT) module to automatically search prompts in the continuous space for FSEC and finally predict the corresponding event types of the inputs. We conduct extensive experiments on two public English datasets for FSEC, i.e., FewEvent and RAMS. The experimental results show the superiority of our proposal over the competitive baselines, where the maximum accuracy increase compared to the strongest baseline reaches 10.8%.     

浅谈对专业化舞台灯光的认识

结合多个灯光设计案例,阐述对专业化舞台灯光的认识,包括如何推动高品质、高质量的专业化舞台灯光发展,当代灯光技术进步对灯光设计师提出的新要求、对灯光设备及灯光厂家的要求等。     

基于应用型人才的实用英语修辞学教育探索

经济新常态下,社会发展对于人才的需求不断提高,应用型人才开始在各个领域崭露头角,迅速成为高校乃至职业院校人才培养的重点。从应用型人才培养的角度,实用英语修辞学教育有着非常积极的作用,应该得到足够的重视。本文从应用型人才以及实用英语修辞学课程的内涵出发,就基于应用型人才的实用英语修辞学教学策略进行了分析和讨论,希望能够促进教学水平的提高。