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).     

物理课的预习方法

物理课的预习是课堂学习的“添加”和“催化”剂,现给同学们介绍几种预习方法,供参考。1标号法在上新课之前,大家应认真阅读新课,并且在课文中的重点句子下面标符号。例如,在预习《光的直线传播》这一课时,在“光在同一种物质里传播的路线是直的”这句话中“同一种物质”,“直”这六个字下面画小黑点,或画红线。又如在预习《电压》这一课时,对“电压是使自由电荷发生定向移动形成电流的原因”这句话不理解,可在这句话的后面画一个问号,以便上课时集中精力听老师讲解其含义,或者向老师提出此问题,请老师解答。2分析法上新课前同学们应根据本…     

关于算子(.V)的广义四则运算的图象实现

本文主要给出实数集R上的一般Fuzzy集关于算子(·V)的四则运算的作图方法.     

从孔子相貌引出的历史研究

岳麓版必修三第一课是“孔子与老子”，在研究了2007年广东高考历史试题的基础上，我对本课教学设计了这样的环节：     

"苯酚的性质"(第二课时)课堂教学实录与评析

1课题提出笔者于2007年4月中旬在无锡召开的《第一届全国苏教版高中〈化学〉教材培训会》上,代表苏州市开设了一节《有机化学基础》模块内容的公开展示课,课题是"苯酚的性质(第二课时)"。作为有机化学一个典型物质的性质新授课,一般的习惯套路是"分子结构——物理性质——化学性质——     

教学本质论

从促进学生心理发展的角度看,教学的本质是一种特殊的认识过程、一种特殊的交往过程、一种自我反思过程、一种动力生成过程。     

推动广东省先进制造业和现代服务业融合发展

广东作为制造业大省,在先进制造业与现代服务深度融合发展方面先试先行,经过一系列行之有效的政策举措,两业融合发展取得一定进展,但在两业融合发展推进过程中也面临诸多困难和挑战.在国际贸易保护主义上升、全球市场萎缩、新冠疫情蔓延的外部环境下以及新一轮科技革命和产业变革的激烈角逐中,如何促进产业间配合、推动制造业和服务业融合发...     

Transportation of AIE-visualized nanoliposomes is dominated by the protein corona

Liposomes, especially cationic liposomes, are the most common and well-investigated nanocarriers for biomedical applications, such as drug and gene delivery. Like other types of nanomaterials, once liposomes are incubated in a biological milieu, their surface can be immediately cloaked by biological components to form a protein corona, which confers a new ‘biological identity’ and modulates downstream interactions with cells. However, it remains unclear how the protein corona affects the transportation mechanism after liposomes interact with cells. Here, we employed home-made aggregation-induced-emission-visualized nanoliposomes TR4@Lipo as a model to investigate transportation with or without the protein corona by optical imaging techniques. The results show that the protein corona can change the cellular transportation mechanism of TR4@Lipo from energy-independent membrane fusion to energy-dependent endocytosis. The protein corona also modulates the intracellular distribution of loaded cargoes. This knowledge furthers our understanding of bio-nano interactions and is important for the efficient use of cationic liposomes.     

霓虹灯PLC控制与监控组态设计

本文介绍了霓虹灯PLC控制与监控组态设计过程。本仿真系统是由上位机和下位机两部分组成,上位机内装MCGS组态软件,主要用来完成仿真界面的制作工作,而下位机则主要用来完成PLC程序的编写。通过串行连接、调试,成功在上位机中模拟出霓虹灯的运行情况。本文对PLC实验教学及科研人员的研发工作有一定的参考价值。