Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-...Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.展开更多
Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19)...Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.展开更多
The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems.The exponential generation of text data on the Inte...The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems.The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights.The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users.The Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the data.The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)model.The presented ALTS-MLODL technique aims to summarize the text documents in the English language.To accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of features.Next,the MLO algorithm is used for the effectual selection of the extracted features.For the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter optimizer.The exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization results.To validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were conducted.The experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.展开更多
The demands placed on the legal document writers and translators （Chinese, English） in creating and organizing faithful legal documents are well-recognized in recent years. In order to achieve a satisfactory result,...The demands placed on the legal document writers and translators （Chinese, English） in creating and organizing faithful legal documents are well-recognized in recent years. In order to achieve a satisfactory result, the legal document writers must be fully aware of the prominent characteristics of the legal documents and the translators are compelled to have a good command of the complexities of the stylistic features of the two different legal documents in contrast. This paper analyses five Chinese legal documents and two English legal documents, following a framework synthesized from contrastive and stylistic analysis. Eight findings are discovered from the analysis concerning lexical, grammatical and textual features of the legal language, attempting to provide an opportunity for the legal document writers and translators to gain further insight into the contrastive features between Chinese and English legal languages as well as their respective stylistic features.展开更多
Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that...Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.展开更多
Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions.The number of soci...Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions.The number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a betterway.Irony and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion mining.For the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web content.Therefore,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social media.The presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social media.To attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature extraction.For irony detection and classification,the DBN model is exploited in this work.At last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)algorithm.The experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark dataset.The simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.展开更多
During the past two to three decades, developments in the fields of transformational grammar, general and contrastive linguistics, semantics, pragmatics, information theory, anthropology, semiotics, psychology and dis...During the past two to three decades, developments in the fields of transformational grammar, general and contrastive linguistics, semantics, pragmatics, information theory, anthropology, semiotics, psychology and discourse analysis etc., have produced great influence on general translation theory, enabling the discipline to broaden the areas of investigation and to offer fresh insights into the concept of correspondence on transference between linguistic and cultural systems. Through this essay, the author would like to cast some lights on the relationship between translation and semiotics and tries to predict the developing trend of translation criteria from the angle of sociosemiotics.展开更多
Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking an...Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.展开更多
We used a sentence-picture matching task to demonstrate that heuristics can influence language comprehension. Interpretation of quantifier scope ambiguous sentences such as Every kid climbed?a tree was investigated. S...We used a sentence-picture matching task to demonstrate that heuristics can influence language comprehension. Interpretation of quantifier scope ambiguous sentences such as Every kid climbed?a tree was investigated. Such sentences are ambiguous with respect to the number of trees inferred;either several trees were climbed or just one. The availability of the NOUN VERB NOUN (N-V-N) heuristic, e.g., KID CLIMB TREE, should contribute to the interpretation of how many trees were climbed. Specifically, we hypothesized that number choices for these stimuli would be predicted by choices previously made to corresponding (full) sentences. 45 participants were instructed to treat N-V-N triplets such as KID CLIMB TREE as telegrams and select a picture, regarding the quantity (“several” vs. “one”) associated with tree. Results confirmed that plural responses to quantifier scope ambiguous sentences significantly predict increased plural judgments in the picture-matching task. This result provides empirical evidence that the N-V-N heuristic, via conceptual event knowledge, can influence sentence interpretation. Furthermore, event knowledge must include the quantity of participants in the event (especially in terms of “several” vs. “one”). These findings are consistent with our model of language comprehension functioning as “Heuristic first, algorithmic second.” Furthermore, results are consistent with judgment and decision making in other cognitive domains.展开更多
Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent days.ABSA refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects...Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent days.ABSA refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided text.Most of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as lexicons.In literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and tools.These sources were used for processing and analyzing the Arabic language content like lexicons.However,an important challenge in this domain is the unavailability of sufficient and reliable resources.In this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)technique.The aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic language.In the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful format.Besides,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of sentiments.Furthermore,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM model.This scenario establishes the novelty of current study.The performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.展开更多
Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare...Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so on.Emotion is expressed in several means,like facial and speech expressions,gestures,and written text.Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)domains.This article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this study.The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets.At the introductory level,the DHODBN-EC technique pre-processes the tweets at different levels.Besides,the word2vec feature extraction process is applied to generate the word embedding process.For emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class labels.Lastly,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique.An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.展开更多
The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spr...The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spread data with minimal examination and filters freely.Due to this,the old problem of fake news has resurfaced.It has become an important concern due to its negative impact on the community.To manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)techniques.To perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite effectively.Still,a huge effort is required from the human side to generate the labelled training data.The recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news detection.To unlock social media data,an automatic text classifier is highly helpful in the domain of NLP.The current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)approach.The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news.To attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding process.Besides,the QCLSTM model is utilized for classification.To boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the hyperparameters.The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset.The QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.展开更多
Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in text.Computational linguistics aims t...Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in text.Computational linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural language.Sentiment is classified as a negative or positive assessment articulated through language.SA can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been reviewed.Deep learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise SA.With this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)technique.The major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review data.Primarily,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding process.For the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this work.At last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different datasets.The comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.展开更多
Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns.Emails stay in the leading positions for business as well as personal us...Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns.Emails stay in the leading positions for business as well as personal use.This popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email assaults.Email filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the end-users.But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced.Thus,this study provides a solution related to email message body text automatic classification into phishing and email spam.This paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on Applied Linguistics for Email Classification.The presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam emails.At the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further computation.Next,the SRRNN method can be useful in recognizing and classifying spam emails.As hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter optimizer.To investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.展开更多
This study aimed to examine some coursebooks of English as a Foreign Language （EFL） to see whether they involve any cross-cultural topics belonging to different cultures from different countries in the world. Assumi...This study aimed to examine some coursebooks of English as a Foreign Language （EFL） to see whether they involve any cross-cultural topics belonging to different cultures from different countries in the world. Assuming that EFL coursebooks written after the communicative movements in foreign language teaching and learning in the 1970s and the 1980s would have a plenty of cross-cultural elements, it was expected to see these coursebooks would also have a lot of cross-cultural elements, especially regarding their publication dates. This study aimed to investigate to what extent teaching materials used in EFL setting involve intercultural elements. Accordingly, 5 coursebooks were evaluated, and topics and number of cross-cultural elements were presented. The coursebooks used in the study were published in 1998, 1999, 1999, 2001 and 2006 namely. However, as the results displayed, the frequency of cross-cultural elements were not mutually related to their publication dates. The distribution and frequency of cross-cultural elements were not balanced with the dates chronologically.展开更多
ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN t...ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.展开更多
Many translation critics have com-mented on The Story of the Stone,DavidHawkes’s translation of the Chineseclassical novel《红楼梦》written by CaoXueqin two centuries ago.Some of thesecomments are favourable.For inst...Many translation critics have com-mented on The Story of the Stone,DavidHawkes’s translation of the Chineseclassical novel《红楼梦》written by CaoXueqin two centuries ago.Some of thesecomments are favourable.For instance,Jin Di and Nida(1984:95—98)and JinDi(1989:7)cite passages from it as exam-ples to illustrate functional equivalence orthe artistry of translating,and Lin Yiliang(1976:118—119)remarks that展开更多
Based on the analyses on the quality of educational periodicals and the number of publications and citations in Chinese Social Sciences Citation Index(CSSCI),this paper intends to make an analysis and introduction of ...Based on the analyses on the quality of educational periodicals and the number of publications and citations in Chinese Social Sciences Citation Index(CSSCI),this paper intends to make an analysis and introduction of the general situation of Chinese educational journals and publications during 2000–2004.Results show that the quantity of educational periodicals and papers published in China,their influence,“impact factor”and the quantity of language varieties and quotation types are not completely compatible.展开更多
Background Sexual and reproductive health among adolescents have become increasingly important and aroused international concerns. In this study, we investigate sexual knowledge, attitudes, sexual behaviors, the unwan...Background Sexual and reproductive health among adolescents have become increasingly important and aroused international concerns. In this study, we investigate sexual knowledge, attitudes, sexual behaviors, the unwanted pregnancy and the abortion rate and to explore related determinants among college students in Beijing. Methods This study is based on a cross-sectional survey of college students＇ knowledge, attitudes and behavior. Multistage cluster sampling was used to select subjects in Beijing. The self-questionnaire designed.by our research group including general information, knowledge, attitude and behavior about sexuality was used to collect information. A total of 2003 questionnaires were collected from June to July 2010. Results The data showed that most of the college students lacked knowledge about reproductive health. Only 17.9% of the respondents knew the appropriate time of abortion. Data also showed that the respondents had high-risk attitude about sex, 58.7% could accept premarital sex, and 29.7% had negative attitude towards contraception. Moreover, sexual activity of the respondents was active. Data showed that 18.5% of the respondents had had sexual activities. Significantly more boys than girls had sex （Х^2=73.374, P 〈0.001）. Among the boys and girls who reported sexual history, 43.1% of the boys had impregnated girlfriend and 49.3% of the girls among those people who have sex had unwanted pregnancies. Logistic regression analysis showed that the variables the gender （OR=3.12, 95% CI： 2.39-4.11）, grade （OR=1.78, 95% CI： 1.40-2.26）, specialty （OR=1.35, 95% CI： 1.12-1.74）, family situation （OR=1.66, 95% CI： 1.15-2.38）, score of knowledge （OR=0.74, 95% CI： 0.58-0.95） and attitude to sex activity （OR=0.09, 95% CI： 0.04-0.22） had a significant effect on having sexual behavior. Conclusions College students lack knowledge and methods students have high-risk sexual attitude and behaviors. Therefore college students would be strongly recommended. to avoid risky sexual behaviors in Beijing. College suitable and effective sex health measures to protect展开更多
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+1 种基金Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4331004DSR031)supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).
文摘Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech therapy.The ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate speech.Amongst the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the Internet.Based on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and profanity.Amongst these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or violence.Machine Learning(ML)techniques have recently been applied to recognize hate speech-related content.The current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social media.The GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate speech.In the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding processes.Then,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and classification.At last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition process.To depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was conducted.The comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.
基金The authors thank the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under grant number(120/43)Princess Nourah bint Abdulrahman UniversityResearchers Supporting Project number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research atUmmAl-Qura University for supporting this work by Grant Code:(22UQU4331004DSR06).
文摘Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Ara-biaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR09).
文摘The term‘executed linguistics’corresponds to an interdisciplinary domain in which the solutions are identified and provided for real-time language-related problems.The exponential generation of text data on the Internet must be leveraged to gain knowledgeable insights.The extraction of meaningful insights from text data is crucial since it can provide value-added solutions for business organizations and end-users.The Automatic Text Summarization(ATS)process reduces the primary size of the text without losing any basic components of the data.The current study introduces an Applied Linguistics-based English Text Summarization using a Mixed Leader-Based Optimizer with Deep Learning(ALTS-MLODL)model.The presented ALTS-MLODL technique aims to summarize the text documents in the English language.To accomplish this objective,the proposed ALTS-MLODL technique pre-processes the input documents and primarily extracts a set of features.Next,the MLO algorithm is used for the effectual selection of the extracted features.For the text summarization process,the Cascaded Recurrent Neural Network(CRNN)model is exploited whereas the Whale Optimization Algorithm(WOA)is used as a hyperparameter optimizer.The exploitation of the MLO-based feature selection and the WOA-based hyper-parameter tuning enhanced the summarization results.To validate the perfor-mance of the ALTS-MLODL technique,numerous simulation analyses were conducted.The experimental results signify the superiority of the proposed ALTS-MLODL technique over other approaches.
文摘The demands placed on the legal document writers and translators （Chinese, English） in creating and organizing faithful legal documents are well-recognized in recent years. In order to achieve a satisfactory result, the legal document writers must be fully aware of the prominent characteristics of the legal documents and the translators are compelled to have a good command of the complexities of the stylistic features of the two different legal documents in contrast. This paper analyses five Chinese legal documents and two English legal documents, following a framework synthesized from contrastive and stylistic analysis. Eight findings are discovered from the analysis concerning lexical, grammatical and textual features of the legal language, attempting to provide an opportunity for the legal document writers and translators to gain further insight into the contrastive features between Chinese and English legal languages as well as their respective stylistic features.
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR10).
文摘Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(120/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R281)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4320484DSR33).
文摘Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic questions.The number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a betterway.Irony and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion mining.For the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web content.Therefore,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social media.The presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social media.To attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature extraction.For irony detection and classification,the DBN model is exploited in this work.At last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)algorithm.The experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark dataset.The simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.
文摘During the past two to three decades, developments in the fields of transformational grammar, general and contrastive linguistics, semantics, pragmatics, information theory, anthropology, semiotics, psychology and discourse analysis etc., have produced great influence on general translation theory, enabling the discipline to broaden the areas of investigation and to offer fresh insights into the concept of correspondence on transference between linguistic and cultural systems. Through this essay, the author would like to cast some lights on the relationship between translation and semiotics and tries to predict the developing trend of translation criteria from the angle of sociosemiotics.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(120/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR32).
文摘Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.
文摘We used a sentence-picture matching task to demonstrate that heuristics can influence language comprehension. Interpretation of quantifier scope ambiguous sentences such as Every kid climbed?a tree was investigated. Such sentences are ambiguous with respect to the number of trees inferred;either several trees were climbed or just one. The availability of the NOUN VERB NOUN (N-V-N) heuristic, e.g., KID CLIMB TREE, should contribute to the interpretation of how many trees were climbed. Specifically, we hypothesized that number choices for these stimuli would be predicted by choices previously made to corresponding (full) sentences. 45 participants were instructed to treat N-V-N triplets such as KID CLIMB TREE as telegrams and select a picture, regarding the quantity (“several” vs. “one”) associated with tree. Results confirmed that plural responses to quantifier scope ambiguous sentences significantly predict increased plural judgments in the picture-matching task. This result provides empirical evidence that the N-V-N heuristic, via conceptual event knowledge, can influence sentence interpretation. Furthermore, event knowledge must include the quantity of participants in the event (especially in terms of “several” vs. “one”). These findings are consistent with our model of language comprehension functioning as “Heuristic first, algorithmic second.” Furthermore, results are consistent with judgment and decision making in other cognitive domains.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR52。
文摘Aspect-Based Sentiment Analysis(ABSA)on Arabic corpus has become an active research topic in recent days.ABSA refers to a fine-grained Sentiment Analysis(SA)task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided text.Most of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as lexicons.In literature,concerning the Arabic language text analysis,the authors made use of regular Machine Learning(ML)techniques that rely on a group of rare sources and tools.These sources were used for processing and analyzing the Arabic language content like lexicons.However,an important challenge in this domain is the unavailability of sufficient and reliable resources.In this background,the current study introduces a new Battle Royale Optimization with Fuzzy Deep Learning for Arabic Aspect Based Sentiment Classification(BROFDL-AASC)technique.The aim of the presented BROFDL-AASC model is to detect and classify the sentiments in the Arabic language.In the presented BROFDL-AASC model,data pre-processing is performed at first to convert the input data into a useful format.Besides,the BROFDL-AASC model includes Discriminative Fuzzy-based Restricted Boltzmann Machine(DFRBM)model for the identification and categorization of sentiments.Furthermore,the BRO algorithm is exploited for optimal fine-tuning of the hyperparameters related to the FBRBM model.This scenario establishes the novelty of current study.The performance of the proposed BROFDL-AASC model was validated and the outcomes demonstrate the supremacy of BROFDL-AASC model over other existing models.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaDeanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: (22UQU4340237DSR61).
文摘Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so on.Emotion is expressed in several means,like facial and speech expressions,gestures,and written text.Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)domains.This article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this study.The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets.At the introductory level,the DHODBN-EC technique pre-processes the tweets at different levels.Besides,the word2vec feature extraction process is applied to generate the word embedding process.For emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class labels.Lastly,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique.An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R281)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR41).
文摘The term‘corpus’refers to a huge volume of structured datasets containing machine-readable texts.Such texts are generated in a natural communicative setting.The explosion of social media permitted individuals to spread data with minimal examination and filters freely.Due to this,the old problem of fake news has resurfaced.It has become an important concern due to its negative impact on the community.To manage the spread of fake news,automatic recognition approaches have been investigated earlier using Artificial Intelligence(AI)and Machine Learning(ML)techniques.To perform the medicinal text classification tasks,the ML approaches were applied,and they performed quite effectively.Still,a huge effort is required from the human side to generate the labelled training data.The recent progress of the Deep Learning(DL)methods seems to be a promising solution to tackle difficult types of Natural Language Processing(NLP)tasks,especially fake news detection.To unlock social media data,an automatic text classifier is highly helpful in the domain of NLP.The current research article focuses on the design of the Optimal Quad ChannelHybrid Long Short-Term Memory-based Fake News Classification(QCLSTM-FNC)approach.The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news.To attain this,the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glovebased word embedding process.Besides,the QCLSTM model is utilized for classification.To boost the classification results of the QCLSTM model,a Quasi-Oppositional Sandpiper Optimization(QOSPO)algorithm is utilized to fine-tune the hyperparameters.The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset.The QCLSTMFNC approach successfully outperformed all other existing DL models under different measures.
基金Supporting Project Number(PNURSP2022R281),Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4320484DSR08).
文摘Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in text.Computational linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural language.Sentiment is classified as a negative or positive assessment articulated through language.SA can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been reviewed.Deep learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise SA.With this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)technique.The major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review data.Primarily,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding process.For the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this work.At last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different datasets.The comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,SaudiArabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR31).
文摘Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns.Emails stay in the leading positions for business as well as personal use.This popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email assaults.Email filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the end-users.But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced.Thus,this study provides a solution related to email message body text automatic classification into phishing and email spam.This paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on Applied Linguistics for Email Classification.The presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam emails.At the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further computation.Next,the SRRNN method can be useful in recognizing and classifying spam emails.As hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter optimizer.To investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.
文摘This study aimed to examine some coursebooks of English as a Foreign Language （EFL） to see whether they involve any cross-cultural topics belonging to different cultures from different countries in the world. Assuming that EFL coursebooks written after the communicative movements in foreign language teaching and learning in the 1970s and the 1980s would have a plenty of cross-cultural elements, it was expected to see these coursebooks would also have a lot of cross-cultural elements, especially regarding their publication dates. This study aimed to investigate to what extent teaching materials used in EFL setting involve intercultural elements. Accordingly, 5 coursebooks were evaluated, and topics and number of cross-cultural elements were presented. The coursebooks used in the study were published in 1998, 1999, 1999, 2001 and 2006 namely. However, as the results displayed, the frequency of cross-cultural elements were not mutually related to their publication dates. The distribution and frequency of cross-cultural elements were not balanced with the dates chronologically.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia+1 种基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4210118DSR33The authors are thankful to the Deanship of ScientificResearch atNajranUniversity for funding thiswork under theResearch Groups Funding Program Grant Code(NU/RG/SERC/11/7).
文摘ive Arabic Text Summarization using Hyperparameter Tuned Denoising Deep Neural Network(AATS-HTDDNN)technique.The presented AATS-HTDDNN technique aims to generate summaries of Arabic text.In the presented AATS-HTDDNN technique,the DDNN model is utilized to generate the summary.This study exploits the Chameleon Swarm Optimization(CSO)algorithm to fine-tune the hyperparameters relevant to the DDNN model since it considerably affects the summarization efficiency.This phase shows the novelty of the current study.To validate the enhanced summarization performance of the proposed AATS-HTDDNN model,a comprehensive experimental analysis was conducted.The comparison study outcomes confirmed the better performance of the AATS-HTDDNN model over other approaches.
文摘Many translation critics have com-mented on The Story of the Stone,DavidHawkes’s translation of the Chineseclassical novel《红楼梦》written by CaoXueqin two centuries ago.Some of thesecomments are favourable.For instance,Jin Di and Nida(1984:95—98)and JinDi(1989:7)cite passages from it as exam-ples to illustrate functional equivalence orthe artistry of translating,and Lin Yiliang(1976:118—119)remarks that
文摘Based on the analyses on the quality of educational periodicals and the number of publications and citations in Chinese Social Sciences Citation Index(CSSCI),this paper intends to make an analysis and introduction of the general situation of Chinese educational journals and publications during 2000–2004.Results show that the quantity of educational periodicals and papers published in China,their influence,“impact factor”and the quantity of language varieties and quotation types are not completely compatible.
文摘Background Sexual and reproductive health among adolescents have become increasingly important and aroused international concerns. In this study, we investigate sexual knowledge, attitudes, sexual behaviors, the unwanted pregnancy and the abortion rate and to explore related determinants among college students in Beijing. Methods This study is based on a cross-sectional survey of college students＇ knowledge, attitudes and behavior. Multistage cluster sampling was used to select subjects in Beijing. The self-questionnaire designed.by our research group including general information, knowledge, attitude and behavior about sexuality was used to collect information. A total of 2003 questionnaires were collected from June to July 2010. Results The data showed that most of the college students lacked knowledge about reproductive health. Only 17.9% of the respondents knew the appropriate time of abortion. Data also showed that the respondents had high-risk attitude about sex, 58.7% could accept premarital sex, and 29.7% had negative attitude towards contraception. Moreover, sexual activity of the respondents was active. Data showed that 18.5% of the respondents had had sexual activities. Significantly more boys than girls had sex （Х^2=73.374, P 〈0.001）. Among the boys and girls who reported sexual history, 43.1% of the boys had impregnated girlfriend and 49.3% of the girls among those people who have sex had unwanted pregnancies. Logistic regression analysis showed that the variables the gender （OR=3.12, 95% CI： 2.39-4.11）, grade （OR=1.78, 95% CI： 1.40-2.26）, specialty （OR=1.35, 95% CI： 1.12-1.74）, family situation （OR=1.66, 95% CI： 1.15-2.38）, score of knowledge （OR=0.74, 95% CI： 0.58-0.95） and attitude to sex activity （OR=0.09, 95% CI： 0.04-0.22） had a significant effect on having sexual behavior. Conclusions College students lack knowledge and methods students have high-risk sexual attitude and behaviors. Therefore college students would be strongly recommended. to avoid risky sexual behaviors in Beijing. College suitable and effective sex health measures to protect
