Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic interest.Scholars have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs engender.To clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature review.The goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)users.The final review included 133 articles.Dominant research themes include question quality,answer quality,and expert identification.In terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack Overflow.The scope of most articles was confined to just one platform with few cross-platform investigations.Articles with ML outnumber those with DL.Nonetheless,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

The Analysis of the Thematic Progression Patterns in “The Great Learning”

This paper intends to introduce briefly the thematic progression patterns in Systemic- Functional Grammar, then analyze its application in "The Great Learning" which is one of the classics of the Confucius and his disciples. The analysis of the thematic progression patterns of "The Great Learning" is meaningful for both understanding and appreciating "The Great Learning".

Cornelius Cardew实验音乐作品《The Great learning》中的中国儒家思想研究

《The Great Learning》是卡迪尤创作的乐队和人声的非标准型态的作品,分为七个段落,创作素材来源于中国古老儒家著作《大学章句·序》中的七句话。本文对实验音乐发展的历程进行梳理,再探究Cornelius Cardew实验音乐作品《The Great learning》中运用的音乐素材与中国儒家思想的联系。

A survey of deep learning-based visual question answering

With the warming up and continuous development of machine learning,especially deep learning,the research on visual question answering field has made significant progress,with important theoretical research significance and practical application value.Therefore,it is necessary to summarize the current research and provide some reference for researchers in this field.This article conducted a detailed and in-depth analysis and summarized of relevant research and typical methods of visual question answering field.First,relevant background knowledge about VQA(Visual Question Answering)was introduced.Secondly,the issues and challenges of visual question answering were discussed,and at the same time,some promising discussion on the particular methodologies was given.Thirdly,the key sub-problems affecting visual question answering were summarized and analyzed.Then,the current commonly used data sets and evaluation indicators were summarized.Next,in view of the popular algorithms and models in VQA research,comparison of the algorithms and models was summarized and listed.Finally,the future development trend and conclusion of visual question answering were prospected.

Understanding the Past,Present,and Future of China's Economic Development——Based on A Unified Framework of Growth Theories

Through exploring the limitation of the neoclassical theory of economic growth,which classifies growth as a homogenous process,this paper reconciles various theories of economic development and explains the rises and falls of economic growth under a unified framework,focusing on incentives of the accumulation of physical and human capital.This paper classifies instances of economic growth into four categories—the Malthusian poverty trap,the Lewis dual model of economic development,the Lewis turning point,and Solow neoclassical growth model.This paper conducts empirical analysis of these categories of economic development as they are relevant to Chinese economic growth and discusses policy implications therein.

Semantic model and optimization of creative processes at mathematical knowledge formation

The aim of this work is mathematical education through the knowledge system and mathematical modeling. A net model of formation of mathematical knowledge as a deductive theory is suggested here. Within this model the formation of deductive theory is represented as the development of a certain informational space, the elements of which are structured in the form of the orientated semantic net. This net is properly metrized and characterized by a certain system of coverings. It allows injecting net optimization parameters, regulating qualitative aspects of knowledge system under consideration. To regulate the creative processes of the formation and realization of mathematical know- edge, stochastic model of formation deductive theory is suggested here in the form of branching Markovian process, which is realized in the corresponding informational space as a semantic net. According to this stochastic model we can get correct foundation of criterion of optimization creative processes that leads to “great main points” strategy (GMP-strategy) in the process of realization of the effective control in the research work in the sphere of mathematics and its applications.

Case and Questions Design in Case- based Learning Used in Medical-nursing English Teaching

Case-based learning(CBL) is gradually replacing the traditional lecturing-based learning in nursing English teaching.In the process of CBL, selecting and compiling a good case is key to the success of CBL. In the meantime, designing questions is an important factor for successful CBL. In this article, we discuss how to select and compile cases and how to design questions in CBL used in Medical-nursing English Teaching.

Professional Learning Communities(PLCs)of Chemistry Teachers

The models of Professional Learning Communities(PLCs)are based on principles of learning that emphasize the co-construction of knowledge by learners,who in this case are the teachers themselves.Teachers in a PLC meet regularly to explore their practices and the learning outcomes of their students,analyze their teaching and their students’learning processes,draw conclusions,and make changes in order to improve their teaching and the learning of their students.It was found that participation in a PLC influences teaching practice,so teachers become more student-centered.Moreover,the teaching culture improves as the community increases the degree of cooperation among teachers,and focuses on the processes of learning rather than the accumulation of knowledge.This enables students to be innovative,creative,and critical.In addition,trust is developed among the participants,which enables them to discuss and analyze their students’cognitive and affective problems,misconceptions,and learning outcomes.

A Study of Teacher Questioning in Interactive English Classroom

This essay presents a study of teacher questioning in interactive English classroom. Interaction plays a key role in second language classroom. Teacher questioning, as one of the teacher initiating activities, could facilitate students＇ language acquisition by asking questions and initiating responses from students. In this essay, two samples of teacher questions were looked into to find out the types, purposes and effectiveness of teacher questions. It was found that the experienced teacher was better at employing teacher questions for interaction than the student teacher. Both of them should improve the questions they asked in classroom. More effective teacher questioning should be introduced according to specific language learning environment by referring to the cognitive level of the students and more opportunities and motivation should be provided for students＇ response.

A Study on Teacher’s Questioning Practice in an ESL Learning Context in Hong Kong

This empirical study intends to explore the questioning behaviors of an English as a second language(hereinafter referred to as ESL)teacher in Hong Kong by quantitatively looking at the distribution of the two types of questions,namely display questions and referential questions,as well as by qualitatively evaluating the universally accepted functions of the questions and the effectiveness of the modification techniques used to enhance the factual value of the questions.Data-based explorations challenging the traditional views toward questions are critically presented,and new findings are excavated and advocated.Pedagogical implications are considerably raised as they serve as a theoretical framework to be applied and further analyzed in future real-life EFL and ESL settings,so as to realize better assessment for learning.

基于渐进机器学习的中文问句匹配方法

问句匹配旨在判断不同问句的意图是否相近.近年来,随着大型预训练语言模型的发展,利用其挖掘问句对在语义层面隐含的匹配信息,取得了目前为止最好的性能.然而,由于基于独立同分布假设,在真实场景中,这些深度学习模型的性能仍然受制于训练数据的充足程度和目标数据与训练数据之间的分布漂移.本文提出一种基于渐进机器学习的中文问句匹配方法.该方法基于渐进机器学习框架,从不同角度提取问句特征,构建融合各类特征信息的因子图,然后通过迭代的因子推理实现从易到难的渐进学习.在特征建模中,设计并实现了两种类型特征的提取:(1)基于TF-IDF(Term frequency-inverse document frequency)的关键词特征;(2)基于DNN(Deep neural network)的深度语义特征.最后,通过通用的基准中文数据集LCQMC和BQ corpus验证了所提方法的有效性.实验表明,相比于单纯的深度学习模型,基于渐进机器学习的方法可以有效提升问句匹配的准确率,且其性能优势随着标签训练数据的减少而增大.

A Novel Bidirectional LSTM and Attention Mechanism Based Neural Network for Answer Selection in Community Question Answering

Deep learning models have been shown to have great advantages in answer selection tasks.The existing models,which employ encoder-decoder recurrent neural network(RNN),have been demonstrated to be effective.However,the traditional RNN-based models still suffer from limitations such as 1)high-dimensional data representation in natural language processing and 2)biased attentive weights for subsequent words in traditional time series models.In this study,a new answer selection model is proposed based on the Bidirectional Long Short-Term Memory(Bi-LSTM)and attention mechanism.The proposed model is able to generate the more effective question-answer pair representation.Experiments on a question answering dataset that includes information from multiple fields show the great advantages of our proposed model.Specifically,we achieve a maximum improvement of 3.8%over the classical LSTM model in terms of mean average precision.

Expert Recommendation in Community Question Answering via Heterogeneous Content Network Embedding

ExpertRecommendation(ER)aims to identify domain experts with high expertise and willingness to provide answers to questions in Community Question Answering(CQA)web services.How to model questions and users in the heterogeneous content network is critical to this task.Most traditional methods focus on modeling questions and users based on the textual content left in the community while ignoring the structural properties of heterogeneous CQA networks and always suffering from textual data sparsity issues.Recent approaches take advantage of structural proximities between nodes and attempt to fuse the textual content of nodes for modeling.However,they often fail to distinguish the nodes’personalized preferences and only consider the textual content of a part of the nodes in network embedding learning,while ignoring the semantic relevance of nodes.In this paper,we propose a novel framework that jointly considers the structural proximity relations and textual semantic relevance to model users and questions more comprehensively.Specifically,we learn topology-based embeddings through a hierarchical attentive network learning strategy,in which the proximity information and the personalized preference of nodes are encoded and preserved.Meanwhile,we utilize the node’s textual content and the text correlation between adjacent nodes to build the content-based embedding through a meta-context-aware skip-gram model.In addition,the user’s relative answer quality is incorporated to promote the ranking performance.Experimental results show that our proposed framework consistently and significantly outperforms the state-of-the-art baselines on three real-world datasets by taking the deep semantic understanding and structural feature learning together.The performance of the proposed work is analyzed in terms of MRR,P@K,and MAP and is proven to be more advanced than the existing methodologies.

一种面向中文自动问答的注意力交互深度学习模型

随着互联网、大数据的飞速发展,以深度神经网络(DNN)为代表的人工智能技术迎来了黄金发展时期,自动问答作为人工智能领域的一个重要分支,也得到越来越多学者的关注。现有网络模型可以提取问题或答案的语义特征,但其一方面忽略了问题与答案之间的语义联系,另一方面也不能从整体上把握问题或答案内部所有字符之间的潜在联系。基于此,提出了两种不同形式的注意力交互模块,即互注意力交互模块和自注意力交互模块,并设计出一套基于所提注意力交互模块的深度学习模型,用于证明该注意力交互模块的有效性。首先将问题和答案中的每个字符映射成固定长度的向量,分别得到问题和答案对应的字嵌入矩阵;然后将字嵌入矩阵送入注意力交互模块,得到综合考虑问题与答案所有字符之后的字嵌入矩阵,并与之前的字嵌入矩阵相加,送入深度神经网络模块,用于提取问题与答案的语义特征;最后得到问题与答案的向量表示并计算两者之间的相似度。实验结果表明,所提模型的Top-1准确度较主流深度学习模型最高提升了3.55%,证明了所提注意力交互模块对于改善上述问题的有效性。

经内窥镜大隐静脉获取术在冠状动脉旁路移植术中应用的学习曲线

【目的】探讨内窥镜获取大隐静脉在冠状动脉旁路移植术中的应用并探讨学习曲线,重点关注初学者易发生的问题及对早期临床结果的影响。【方法】回顾性分析2013年7月至2014年4月在郑州大学第一附属医院心外科接受不停跳冠状动脉旁路移植术并使用内窥镜技术获取大隐静脉的83例患者的临床资料,按照手术时间的先后顺序分为A组(初学组20例)、B组(熟悉组20例)、C组(进步组20例)、D组(成熟组23例),分析各组之间患者围术期及随访结果差异,明确学习曲线周期。【结果】该组患者年龄为(60.22±8.06)岁,体质量为(69.77±11.66)kg,其中合并高血压24例、糖尿病26例、亚急性脑梗14例。A组相对于后三组获取大隐静脉长度与时间比值明显较小(P<0.001),静脉主干损伤数量明显较多(P=0.006),并且随访1年时静脉桥通畅率较低,但差异无统计学意义。【结论】内窥镜获取大隐静脉之前技术操作培训是必要的,能有效规避初学者造成的血管损伤,实际获取的过程中大概需要亲自操作20例,并认真总结技术技巧就可以较为熟练地进行相关操作。

反绎学习支持下的自动问答及其应用

自动问答技术可以为用户提供快速且准确的信息检索和问题解答服务。然而,目前常见方法生成的答案存在不准确和不完整的问题,以及实体识别和关系抽取效果不准确,且答案不够自然。为此,提出基于反绎学习的自动问答方法,使用基于知识图谱的问答推理优化基于生成的问答,进一步从整体的反绎学习框架角度来优化实体识别和关系抽取方法,并将所提方法应用于《数据结构》课程的学习。结果表明,基于反绎学习的自动问答方法,可以改进基于生成的问答和基于知识图谱的问答两者的不足,提高问答系统的准确性。

新时代学习型大国建设论要

新时代学习型大国建设的理论来源于马克思主义经典作家的学习型国家理念、中华传统学习型国家智慧、毛泽东等国家领导人的学习型国家思想和国外学习型国家前沿理论。党和政府应对国际风险挑战、实现奋斗目标、满足人民群众终身学习需求共同组成新时代学习型大国建设的现实意蕴。新时代学习型大国建设应健全学习型个人、家庭、企业和政党,促进人人能学、乐学、善学、勤学,自觉向书本、向实践、向历史、向国外学,加强政策、经费、继续教育和信息技术等供给。

基于大语言模型的Linux课程问答系统

基于国产主流大语言模型,设计一个Linux课程知识问答系统.该系统结合检索增强技术,能根据人类反馈持续学习,有助于解决Linux课程教学中如何更有效辅助学生学习的问题.实验结果表明,该系统提高了大语言模型回答的事实性,能有效回答学生提问.此外,该系统以较低成本积累了以自然语言形式呈现的专业领域知识库,降低了教师教学资料搜集整理的工作量.

人工智能背景下代谢生物化学知识树问题链构建

通过设计层层递进的问题链构建代谢生物化学知识树,以提高学生的自主学习能力和批判性思维能力。以“蛋白质的消化吸收与氨基酸代谢”章节为例,教师通过设置基础、进阶、应用和综合层次的问题,引导学生从基础知识到综合应用逐步构建系统的知识结构。结合生成式人工智能技术,帮助学生在自主探索中保持学习兴趣和注意力。这种方法的应用不仅增强了课堂互动性和有效性,也为医学教育的发展提供新思路。

主观题自动评判算法研究综述

在教育教学中,试卷评判是教师获取学生知识点掌握情况的重要途径。然而,试题评分是一个耗时的过程,主观题的评判更需要阅卷人认真、投入、细致地审阅,需要耗费大量精力。要减轻教师工作压力,提高主观题评判的效率,基于人工智能的自动评判技术非常重要,其中主观题的自动评判是难点。随着机器学习和深度学习等技术在自然语言处理领域的发展,主观题自动评判技术有了较大进展。文中将主观题分为常规型和开放型两类进行文献梳理,总结主观题自动评价的标准和公开数据集,归纳涉及的方法和技术路线,并对主观题自动评判技术未来的研究方向进行总结和展望。