Using word embeddings in Twitter election classification

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models).     

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一种基于反向连接技术的穿越防火墙的解决方案

利用一般防火墙不会禁止从计算机内部主动向外部的连接,以及不会禁止关键进程svchost.exe的特点,设计并实现了一个能穿越防火墙的木马程序,具有一定的研究价值.     

中学生主体性与心理健康的相关研究

运用ZTX和MHT量表对郑州市两所中学学生进行问卷调查结果表明：中学生主体性水平与其心理健康水平存在显著负相关;中学生主体性水平越高,其心理健康水平则越高。     

临床案例讨论课在康复治疗技术专业基础医学教学中的应用

文章主要介绍一种与临床紧密联系的课堂,运用于康复治疗技术专业基础医学教学中。该课堂名为"临床案例讨论课",属于实践课。其开设目的在于扩展学生知识面,提高其自学能力,在巩固强化所学知识的基础上帮助学生查漏补缺、夯实基础。组织形式上采取医院查房与课堂讨论相结合的方式,加入案例答辩的新考核环节。临床案例讨论课的开展受到相关专家的肯定和全校师生的好评,但也存在一些不足,成为今后努力的方向。同时对康复课程体系改革和优化教学内容提供反馈性信息。     

论中国古代悼亡诗的伤逝主题及价值取向

悼亡文学是祭悼活动发展到一定阶段的产物 ,其起源与发展从一开始就受社会文化、民族心理、地理环境等因素的影响和制约。悼亡作品中创作主体的情感通常表现为一定的模式 ,这些情感模式展示了传统的爱情观 ,特别是对失落的爱情的态度。而悼亡作品的价值取向又表现了传统的爱情价值观和自我价值观的统一。悼亡作品中独特的情感表达模式和价值取向共同构建起了悼亡文学有别于其它文学的个性特征     

Numerical method to determine mechanical parameters of engineering design in rock masses

INTRODUCTION The historical development of research in rockmechanics has yielded numerous useful and work-able theories. The Coulomb Criterion (1772) andMohr’s Hypothesis (1990) both provide easy andeffective methods for determining mechanical pa-rameters such as rock strength. R.E. Goodmanformulated the equation of mesh and non-meshjoints construction. Other researchers conductedexperiments on different types of rock masses withspecific, unique properties, to solve increasinglyc…     

化工设计课程的教学改革与实践

化工设计是化学工程与工艺专业的最具综合性的课程,能体现复杂工程问题中的基础理论运用能力。通过教学改革,在夯实课程基础及完善课程内容的基础上,通过改善化工设计课程的教学方法,提高授课教师水平,进而在不同层面提升了学生的设计能力和工程思维能力,以达到满足新时代经济社会对人才质量要求的目的。     

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