Analysis of the Features of Network Words

The information society makes people's lives gradually enter a digital state for living. And the popularity of the Internet has led to the unique phenomenon of network words. What impact will network and the combination of language bring about? This article will explore the relation between the phenomenon of network words and social context from the angle of social linguistic through the analysis of network words and grammatical features.

An Effective Machine-Learning Based Feature Extraction/Recognition Model for Fetal Heart Defect Detection from 2D Ultrasonic Imageries

Congenital heart defect,accounting for about 30%of congenital defects,is the most common one.Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns.In Fetal andNeonatal Cardiology,medical imaging technology(2D ultrasonic,MRI)has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis.It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane(FHUSP)manually.Compared withmanual identification,automatic identification through artificial intelligence can save a lot of time,ensure the efficiency of diagnosis,and improve the accuracy of diagnosis.In this study,a feature extraction method based on texture features(Local Binary Pattern LBP and Histogram of Oriented Gradient HOG)and combined with Bag of Words(BOW)model is carried out,and then feature fusion is performed.Finally,it adopts Support VectorMachine(SVM)to realize automatic recognition and classification of FHUSP.The data includes 788 standard plane data sets and 448 normal and abnormal plane data sets.Compared with some other methods and the single method model,the classification accuracy of our model has been obviously improved,with the highest accuracy reaching 87.35%.Similarly,we also verify the performance of the model in normal and abnormal planes,and the average accuracy in classifying abnormal and normal planes is 84.92%.The experimental results show that thismethod can effectively classify and predict different FHUSP and can provide certain assistance for sonographers to diagnose fetal congenital heart disease.

Loan Words in Modem English and Their Features

English language, as a global language, is exerting greater influence on the world. It has been enlarging along with the development of the society, the progress of science and technology by the way of borrowing from other languages such as French, German, Italian, Spanish, Russian, Chinese, Japanese, and Arabic in the fields of politics, culture, education, economics, science, and technology. Borrowing or loan word has become an important part in the process of English vocabulary acquisition. This paper studies modem English loan words, summarizes types of loan words, and makes a tentative analysis of their features with the attempt to facilitate English learning in an effective way.

Hybrid Features for an Arabic Word Recognition System

This research proposes and implements an Arabic Sub-Words Recognition System （ASWR）. The system focuses on employing a combination of statistical and structural features to provide complete pattern＇s description and enhances the recognition rate. Support Vector Machines （SVMs） is utilized as a promising pattern recognition tool. In addition to that, the problems of dots and holes are solved in a completely different way from the ones previously employed. The proposed system proceeds in several phases as follows： （1） image acquisition, （2） binarisation, （3） morphological processing, （4） feature extraction, which includes statistical features, i.e., moment invariants, and structural features, i.e., dot number, dot position, and number of holes, features, and （5） classification, using multi-class SVMs and applying a one-against-all technique. The proposed system has been tested using different sets of words and subwords and has achieved a nearly 98.90% recogiaition rate. Comparative results with NNs are also presented.

Semantic Features and Applications in Translation of English Words in Pairs

English words in pairs are a special form of English idioms, which have different kinds and are used widely. For English learners, words in pairs are one of the difficult points. This paper discusses their form patterns, semantic relations, grammatical functions, rhetoric features and their application in translation. Its purpose is to help learners understand and use them accurately and correctly so as to improve language expressing ability.

Lexical Features of Web-English Words

Since the ARPANET created by the US Department of De-fense occurred in 1969,the internet has gone through a 50-year-or-so history,and now has already become the most widely usedmedia.It is an irrefutable fact that in network communication Eng-lish has beena leading language from the very beginning.Withthe development of the internet technology,web-English has al-ready merged into people’s daily life.This passage will talk aboutthefeaturesofweb-Englishwords.NEOLOGISMThe expansion of vocabulary in modern English dependschiefly on word-formation.There is a variety of means at

Apriori and N-gram Based Chinese Text Feature Extraction Method

A feature extraction, which means extracting the representative words from a text, is an important issue in text mining field. This paper presented a new Apriori and N-gram based Chinese text feature extraction method, and analyzed its correctness and performance. Our method solves the question that the exist extraction methods cannot find the frequent words with arbitrary length in Chinese texts. The experimental results show this method is feasible.

Image Classification Based on the Fusion of Complementary Features

Image classification based on bag-of-words（BOW）has a broad application prospect in pattern recognition field but the shortcomings such as single feature and low classification accuracy are apparent.To deal with this problem,this paper proposes to combine two ingredients：（i）Three features with functions of mutual complementation are adopted to describe the images,including pyramid histogram of words（PHOW）,pyramid histogram of color（PHOC）and pyramid histogram of orientated gradients（PHOG）.（ii）An adaptive feature-weight adjusted image categorization algorithm based on the SVM and the decision level fusion of multiple features are employed.Experiments are carried out on the Caltech101 database,which confirms the validity of the proposed approach.The experimental results show that the classification accuracy rate of the proposed method is improved by 7%-14%higher than that of the traditional BOW methods.With full utilization of global,local and spatial information,the algorithm is much more complete and flexible to describe the feature information of the image through the multi-feature fusion and the pyramid structure composed by image spatial multi-resolution decomposition.Significant improvements to the classification accuracy are achieved as the result.

Content Feature Extraction-based Hybrid Recommendation for Mobile Application Services

The number of mobile application services is showing an explosive growth trend,which makes it difficult for users to determine which ones are of interest.Especially,the new mobile application services are emerge continuously,most of them have not be rated when they need to be recommended to users.This is the typical problem of cold start in the field of collaborative filtering recommendation.This problem may makes it difficult for users to locate and acquire the services that they actually want,and the accuracy and novelty of service recommendations are also difficult to satisfy users.To solve this problem,a hybrid recommendation method for mobile application services based on content feature extraction is proposed in this paper.First,the proposed method in this paper extracts service content features through Natural Language Processing technologies such as word segmentation,part-of-speech tagging,and dependency parsing.It improves the accuracy of describing service attributes and the rationality of the method of calculating service similarity.Then,a language representation model called Bidirectional Encoder Representation from Transformers(BERT)is used to vectorize the content feature text,and an improved weighted word mover’s distance algorithm based on Term Frequency-Inverse Document Frequency(TFIDF-WMD)is used to calculate the similarity of mobile application services.Finally,the recommendation process is completed by combining the item-based collaborative filtering recommendation algorithm.The experimental results show that by using the proposed hybrid recommendation method presented in this paper,the cold start problem is alleviated to a certain extent,and the accuracy of the recommendation result has been significantly improved.

On Lexical Features and Its Applications in Business English Correspondence

The main purposes of this thesis are to carry through a further investigation of the lexical features of business English correspondence and to present the lexical application methods which are based on basic rules in effective business English letter writing.

Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool.

基于BERT的两次注意力机制远程监督关系抽取

针对词向量语义信息不完整以及文本特征抽取时的一词多义问题,提出基于BERT(Bidirectional Encoder Representation from Transformer)的两次注意力加权算法(TARE)。首先,在词向量编码阶段,通过构建Q、K、V矩阵使用自注意力机制动态编码算法,为当前词的词向量捕获文本前后词语义信息;其次,在模型输出句子级特征向量后,利用定位信息符提取全连接层对应参数,构建关系注意力矩阵;最后,运用句子级注意力机制算法为每个句子级特征向量添加不同的注意力分数,提高句子级特征的抗噪能力。实验结果表明:在NYT-10m数据集上,与基于对比学习框架的CIL(Contrastive Instance Learning)算法相比,TARE的F1值提升了4.0个百分点,按置信度降序排列后前100、200和300条数据精准率Precision@N的平均值(P@M)提升了11.3个百分点;在NYT-10d数据集上,与基于注意力机制的PCNN-ATT(Piecewise Convolutional Neural Network algorithm based on ATTention mechanism)算法相比,精准率与召回率曲线下的面积(AUC)提升了4.8个百分点,P@M值提升了2.1个百分点。在主流的远程监督关系抽取(DSER)任务中,TARE有效地提升了模型对数据特征的学习能力。

甲骨文典型细节特征研究

甲骨文为早期成体系的古代文字,字形仍然保留着较多的形象特征,书写大多不太固定,字形往往呈现出一些差异,如笔画多少不一、形体朝向不定、部件不固定、结构不定型等。但其中的关键细节相对比较一致,具体表现在笔画的曲直、笔画的长短、书写的方向、强化的特征、选取的对象以及部件的组合关系等方面。这些有意为之的细节,除了忠实记录着原始信息之外,也有不少出于区分字形和区别字义的考虑。典型细节中蕴含着的重要的构形信息,对字义的传达至为关键,对字形的演变产生了深远的影响。

融合双通道的语义信息的方面级情感分析

针对方面级情感分析任务中语义信息难以提取以及方面词信息难以和上下文信息相关联的问题,提出一种融合双通道的语义信息模型(FDCS)。通过BERT预训练模型搭建两个通道获取不同层次的语义信息,一个是全局信息通道,另一个是句子信息通道;使用语义注意力融合双通道中不同层次的语义信息,将融合后的语义信息再次分别融入全局信息和句子信息;根据每个通道语义信息的不同分别提取相应的特征信息。在3个基准数据集上的实验结果表明,该模型的性能优于其它模型。

基于深度学习的中文短文本多标签分类模型

目前,中文短文本因其长度短、结构多样和缺乏上下文等特点,常规多标签分类算法无法对其有效区分。针对以上问题,论文提出一种基于深度学习的中文短文本多标签分类模型CRC-MHA。CRC-MHA模型在文本表示层摒弃常规使用Word2vec进行静态词嵌入的方式,采用BERT对输入句子进行动态词嵌入,借助海量预训练文本的优势更好地表征文本的上下文语义,同时在特征提取层设计了一种结合CNN、RCNN和多头自注意力机制的并行特征提取策略,加强捕捉文本内部的关键特征来提升多标签分类效果。实验结果表明,CRC-MHA模型在评价指标加权平均F1值上较BERT模型提高1.95%,较BERT-CNN模型提高0.42%,较BERT-RCNN模型提高0.34%,验证了模型的有效性。

我国人工智能政策新词发现与演化研究——一个多特征融合的算法

[目的/意义]作为中文分词的基础研究,新词发现是研究政策创新和扩散的重要技术方法。本文通过改进新词发现算法优化了政策文本分词不准确的问题,并构建词库以支持人工智能政策的演化研究。[方法/过程]提出多特征融合新词发现算法MFF,实现了对人工智能政策新词的挖掘,从新词角度对人工智能政策的创新、延续和扩散进行演化分析。[结果/结论]实验结果证明,本文提出的多特征融合新词发现算法MFF能够有效提升分词效果,丰富领域词库;人工智能政策新词出现的时序变化反映了不同阶段政策关注的重点发展领域,揭示了中央和地方政府在政策创新、延续、扩散和演化方面的特点。

电力设备缺陷文本的双通道语义增强网络挖掘方法

电力设备运维环节积累的缺陷文本可指导设备的状态评价和检修工作。然而缺陷文本结构多样且背景噪声强,导致智能挖掘信息的难度大。针对该问题,提出了基于双通道语义增强网络的电力设备缺陷文本挖掘方法。首先,分析缺陷文本的内容,结合自然语言处理方法预处理缺陷文本。利用Glove词向量嵌入模型将缺陷文本映射至数值空间表征语义。然后,基于词移距离构建缺陷文本的增强文本,通过含注意力机制的双向长短时记忆神经网络分别提取缺陷文本和增强文本的特征,进而在网络末端融合特征实现关键信息加强,提升模型分类性能。实例表明,所提双通道语义增强网络的分类Macro-F1指标相比于传统机器学习方法、单通道深度学习方法至少提高6.2%、5.2%,同时所提方法为实现图像、文本等多源运维数据的特征增强提供新思路。

基于局部Transformer的泰语分词和词性标注联合模型

泰语分词和词性标注任务二者之间存在高关联性,已有研究表明将分词和词性标注任务进行联合学习可以有效提升模型性能,为此,提出了一种针对泰语拼写和构词特点的分词和词性标注联合模型。针对泰语中字符构成音节,音节组成词语的特点,采用局部Transformer网络从音节序列中学习分词特征;考虑到词根和词缀等音节与词性的关联,将用于分词的音节特征融入词语序列特征,缓解未知词的词性标注特征缺失问题。在此基础上,模型采用线性分类层预测分词标签,采用线性条件随机场建模词性序列的依赖关系。在泰语数据集LST20上的试验结果表明,模型分词F1、词性标注微平均F1和宏平均F1分别达到96.33%、97.06%和85.98%,相较基线模型分别提升了0.33%、0.44%和0.12%。

基于注意力增强与特征融合的中文医学实体识别

针对基于字符表示的中文医学领域命名实体识别模型嵌入形式单一、边界识别困难、语义信息利用不充分等问题,一种非常有效的方法是在Bret底层注入词汇特征,在利用词粒度语义信息的同时降低分词错误带来的影响,然而在注入词汇信息的同时也会引入一些低相关性的词汇和噪声,导致基于注意力机制的Bret模型出现注意力分散的情况。此外仅依靠字、词粒度难以充分挖掘中文字符深层次的语义信息。对此,提出基于注意力增强与特征融合的中文医学实体识别模型,对字词注意力分数矩阵进行稀疏处理,使模型的注意力集中在相关度高的词汇,能够有效减少上下文中的噪声词汇干扰。同时,对汉字发音和笔画通过卷积神经网络(CNN)提取特征,经过迭代注意力特征融合模块进行融合,然后与Bret模型的输出特征进行拼接输入给Bi LSTM模型,进一步挖掘字符所包含的深层次语义信息。通过爬虫等方式搜集大量相关医学语料,训练医学领域词向量库,并在CCKS2017和CCKS2019数据集上进行验证,实验结果表明,该模型F1值分别达到94.90%、89.37%,效果优于当前主流的实体识别模型,具有更好的识别效果。

基于深度学习的微博疫情舆情文本情感分析

舆论情感分析重点研究公众对于公共事件的情感偏向,其中涉及公共卫生事件的舆论会直接影响社会稳定,所以对于微博的情感分析尤为重要。该文采取有关疫情方面的文本数据集,使用RoBERTa和BiGRU以及双层Attention结合的RoBERTa-BDA(RoBERTa-BiGRU-Double Attention)模型作为整体结构。首先使用RoBERTa获取了蕴含文本上下文信息的词嵌入表示,其次使用BiGRU得到字符表示,然后使用注意力机制计算各个字符对于全局的影响,再使用BiGRU得到句子表示,最后使用Attention机制计算出每个字符对于其所在的句子的权重占比,得出全文的文本表示,并通过softmax函数对其进行情感分析。为了验证RoBERTa-BDA模型的有效性,设计三种实验,在不同词向量对比实验中,RoBERTa对比BERT中Macro F1和Micro F1值提高了0.42百分点和0.84百分点,在不同特征提取层模型对比实验中,BiGRU-Double Attention对比BiGRU-Attention提高了3.62百分点和1.34百分点,在跨平台对比实验中,RoBERTa-BDA在贴吧平台的Macro F1和Micro F1对比微博平台仅仅降低1.29百分点和2.88百分点。