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
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
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
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
With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their emotions.More and more people are used to commenting on a...With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their emotions.More and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing emotions.Textual Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language processing.It is different from the previous tasks of emotion recognition and emotion classification.In addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion source.In this paper,we provide a survey for TECE.First,we introduce the development process and classification of TECE.Then,we discuss the existing methods and key factors for TECE.Finally,we enumerate the challenges and developing trend for TECE.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
BACKGROUND： In patients with depression, as well as in patients with schizophrenia, both mood and working memory performance are often impaired. Both issues can only be addressed and improved with medication to some ...BACKGROUND： In patients with depression, as well as in patients with schizophrenia, both mood and working memory performance are often impaired. Both issues can only be addressed and improved with medication to some extent. OBJECTIVE： This study investigates the mood and the working memory performance in patients with depression or schizophrenia and whether acupuncture can improve these. DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS： A pragmatic clinical trial design was used The study was conducted in a psychiatric clinic. Fifty patients with depression and 50 with schizophrenia were randomly divided into an experimental and a waiting-list group. Additionally, 25 healthy control participants were included. Twelve weeks of individualized acupuncture treatment was used as the clinical intervention. MAIN OUTCOME MEASURES： All patients were tested before （T1） and after （T2） acupuncture treatment on a mood scale （Beck Depression Inventory-II, BDI-II）, a simple working memory task （digit span）, and a complex working memory task （letter-number sequencing）; the healthy controls were tested at T1 only. RESULTS： Patients with depression scored worse than the others on the BDI-II, and patients with schizophrenia scored worse than the healthy controls. On the digit span, patients with schizophrenia did not differ from healthy controls whereas they scored worse of all on the letter-number sequencing. With respect to the acupuncture findings, first, the present study showed that the use of acupuncture to treat patients with schizophrenia was both practical and safe. Moreover, acupuncture had a positive effect on the BDI-II for the depression group, but acupuncture had no effect on the digit span and on the letter- number sequencing performance for the two clinical groups. CONCLUSION： The clinical improvement in patients with depression after acupuncture treatment was not accompanied by any significant change in a simple working memory task or in a more complex working memory task; the same was true for the patients with schizophrenia.展开更多
The debate on the marketization of discourse in higher education has sparked and sustained interest among researchers in discourse and education studies across a diversity of contexts.While most research in this line ...The debate on the marketization of discourse in higher education has sparked and sustained interest among researchers in discourse and education studies across a diversity of contexts.While most research in this line has focused on marketized discourses such as advertisements,little attention has been paid to promotional discourse in public institutions such as the About us texts on Chinese university websites.The goal of the present study is twofold:first,to describe the generic features of the university About us texts in China;and second,to analyze how promotional discourse is interdiscursively incorporated in the discourse by referring to the broader sociopolitical context.Findings have indicated five main moves:giving an overview,stressing historical status,displaying strengths,pledging political and ideological allegiance,and communicating goals and visions.Move 3,displaying strengths,has the greatest amount of information and can be further divided into six sub-moves which presents information on campus facilities,faculty team,talent cultivation,disciplinary fields construction,academic research,and international exchange.The main linguistic and rhetorical strategies used in these moves are analyzed and discussed.展开更多
Discourse markers(DMs) are characterized by multifunctionality in different contexts.This study addressed the use of the Chinese DM,na(那),as a solution to topical divergence,during clinical interactions with right-he...Discourse markers(DMs) are characterized by multifunctionality in different contexts.This study addressed the use of the Chinese DM,na(那),as a solution to topical divergence,during clinical interactions with right-hemisphere-damaged(RHD) patients.Drawing on data collected from clinical interviews between psychotherapists and RHD patients,this study examined the functions of na in response to RHD topical divergence,focusing on the topic and attitudinal aspects.It was found that na was mainly employed by psychotherapists to mark a reproffer of interview topics(i.e.,an attempt to return to earlier topics),and a display of disalignment and disaffiliation with RHD topical divergence.These functions of na reflect the psychotherapists’ attempts to overcome communicative problems arising from RHD topical divergence,so as to ensure the achievement of the communicative goal.Thus,na can be interpreted as a compensatory strategy for dealing with RHD topical divergence on an interpersonal level.These findings not only expand our knowledge about the function spectrum of na,but also offer insights for RHD patients’ interlocutors to enhance conversational communication with RHD patients via the compensatory strategy.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
Traditional Chinese medicine (TCM) is gaining popularity in the treatment of psychiatric disorders that can be described and treated from either an Eastern or a Western perspective. In Eastern medicine, the disorders ...Traditional Chinese medicine (TCM) is gaining popularity in the treatment of psychiatric disorders that can be described and treated from either an Eastern or a Western perspective. In Eastern medicine, the disorders are described according to five diagnostic methods that are used in TCM: inspection, auscultation, olfaction, inquiry, and palpation, including tongue and pulse diagnosis.展开更多
Embodied semantics theory asserts that the meaning of action-related words is neurally represented through networks that overlap with or are identical to networks involved in sensory-motor processing. While some studi...Embodied semantics theory asserts that the meaning of action-related words is neurally represented through networks that overlap with or are identical to networks involved in sensory-motor processing. While some studies supporting this theory have focused on Chinese characters, less attention has been paid to their semantic radicals. Indeed, there is still disagreement about whether these radicals are processed independently. The present study investigated whether radicals are processed separately and, if so, whether this processing occurs in sensory-motor regions. Materials consisted of 72 high-frequency Chinese characters, with 18 in each of four categories： hand-action verbs with and without hand-radicals, and verbs not related to hand actions, with and without hand-radicals. Twenty-eight participants underwent functional MRI scans while reading the characters. Compared to characters without hand-radicals, reading characters with hand-radicals activated the right medial frontal gyrus. Verbs involving hand-action activated the left inferior parietal lobule, possibly reflecting integration of information in the radical with the semantic meaning of the verb. The findings may be consistent with embodied semantics theory and suggest that neural representation of radicals is indispensable in processing Chinese characters.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
OBJECTIVE: To investigate the effectiveness of acupuncture in the treatment of chronic schizophrenia and co-morbid sleep disorders.METHODS: A 42-year-old German male outpatient,suffering from long-term schizophrenia a...OBJECTIVE: To investigate the effectiveness of acupuncture in the treatment of chronic schizophrenia and co-morbid sleep disorders.METHODS: A 42-year-old German male outpatient,suffering from long-term schizophrenia and sleep disorders, entered the study. Acupuncture was used as a non-pharmacological intervention. In addition to his ongoing Western Medicine(pharmacological) treatment, the patient received 12 weekly(non-standardized) acupuncture treatments in the clinic. The Traditional Chinese Medicine(TCM) diagnosis, the psychological assessment and the actiwatch data were compared before and after the acupuncture treatment.RESULTS: The TCM diagnosis revealed a Liver Fire pattern before the acupuncture treatment, which was still present, although to a lesser degree, after the treatment. The psychological assessment revealed no change in the positive symptoms, but a small decrease in the negative symptoms and the general psychopathology of the patient. This was further illustrated by the small decrease in the number of depressive symptoms. The subjective sleep disorders improved markedly after acupuncture treatment, but the daytime sleepiness did not. The actiwatch results showed that after acupuncture treatment, the patient was moving less during sleep, but no significant results were found for the other sleep parameters.CONCLUSION: Acupuncture was found to be an effective non-pharmacological add-on method for treating subjective sleep disorders, and, to a lesser degree, objective sleep disorders and the negative symptoms of chronic schizophrenia. Future larger clinical trials with follow-up measurements are needed in order to replicate the present preliminary beneficial acupuncture findings and in order to determine whether the observed effects can be sustained.展开更多
For the past several decades, it has been clear that the ability to write texts for academic and professional purposes （＂academic writing＂ in this article） is key to the success of adults in U.S. society--in schoo...For the past several decades, it has been clear that the ability to write texts for academic and professional purposes （＂academic writing＂ in this article） is key to the success of adults in U.S. society--in school, in university courses, on tests that they need to take to progress through learning and into work, and in the workforce. Academic writing has specific features and involves approaches that are different from much of the writing that is done with adult learners, particularly those learning English as an additional language （e.g., described in reviews by Hinkel, 2015; Leki, Cumming, ＆ Silva, 2008; and a survey by Rosenfeld, Courtney, ＆ Fowles, 2004）. However, a recent survey of adult educators, conducted by the authors, found that academic writing has not been a focus in many adult education programs, and teachers receive limited professional development in this area and instructional support in implementing it. This article describes the importance of academic writing at all levels of adult education, the key features of academic writing, and the current state of writing instruction in adult education programs. It then describes the motivation for, design, and outcomes of a survey of and interviews with adult educators across the country on their preparation for and instructional practices with academic writing （conducted in 2014 and 2015）. Finally, it describes approaches that can be used in adult education programs to meet the writing proficiency needs of students at all levels and next steps that the adult education field might take.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
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
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
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 (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.
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.
基金partially supported by the National Natural Science Foundation of China under Grant No.62372121the Ministry of education of Humanities and Social Science project under Grant No.20YJAZH118+1 种基金the National Key Research and Development Program of China under Grant No.2020YFB1005804the MOE Project at Center for Linguistics and Applied Linguistics,Guangdong University of Foreign Studies。
文摘With the rapid development of web technology,Social Networks(SNs)have become one of the most popular platforms for users to exchange views and to express their emotions.More and more people are used to commenting on a certain hot spot in SNs,resulting in a large amount of texts containing emotions.Textual Emotion Cause Extraction(TECE)aims to automatically extract causes for a certain emotion in texts,which is an important research issue in natural language processing.It is different from the previous tasks of emotion recognition and emotion classification.In addition,it is not limited to the shallow-level emotion classification of text,but to trace the emotion source.In this paper,we provide a survey for TECE.First,we introduce the development process and classification of TECE.Then,we discuss the existing methods and key factors for TECE.Finally,we enumerate the challenges and developing trend for TECE.
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BACKGROUND： In patients with depression, as well as in patients with schizophrenia, both mood and working memory performance are often impaired. Both issues can only be addressed and improved with medication to some extent. OBJECTIVE： This study investigates the mood and the working memory performance in patients with depression or schizophrenia and whether acupuncture can improve these. DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS： A pragmatic clinical trial design was used The study was conducted in a psychiatric clinic. Fifty patients with depression and 50 with schizophrenia were randomly divided into an experimental and a waiting-list group. Additionally, 25 healthy control participants were included. Twelve weeks of individualized acupuncture treatment was used as the clinical intervention. MAIN OUTCOME MEASURES： All patients were tested before （T1） and after （T2） acupuncture treatment on a mood scale （Beck Depression Inventory-II, BDI-II）, a simple working memory task （digit span）, and a complex working memory task （letter-number sequencing）; the healthy controls were tested at T1 only. RESULTS： Patients with depression scored worse than the others on the BDI-II, and patients with schizophrenia scored worse than the healthy controls. On the digit span, patients with schizophrenia did not differ from healthy controls whereas they scored worse of all on the letter-number sequencing. With respect to the acupuncture findings, first, the present study showed that the use of acupuncture to treat patients with schizophrenia was both practical and safe. Moreover, acupuncture had a positive effect on the BDI-II for the depression group, but acupuncture had no effect on the digit span and on the letter- number sequencing performance for the two clinical groups. CONCLUSION： The clinical improvement in patients with depression after acupuncture treatment was not accompanied by any significant change in a simple working memory task or in a more complex working memory task; the same was true for the patients with schizophrenia.
基金This study is supported by the Chinese Ministry of Education(MOE)Humanities and Social Science Research Funding(20YJA740050)the MOE Key Research Project of Humanities and Social Science(16JJD740006)conducted by the Center for Linguistics and Applied Linguistics(CLAL),Guangdong University of Foreign Studies(GDUFS).We would like to thank the reviewers for their comments and suggestions on earlier versions of this manuscript.
文摘The debate on the marketization of discourse in higher education has sparked and sustained interest among researchers in discourse and education studies across a diversity of contexts.While most research in this line has focused on marketized discourses such as advertisements,little attention has been paid to promotional discourse in public institutions such as the About us texts on Chinese university websites.The goal of the present study is twofold:first,to describe the generic features of the university About us texts in China;and second,to analyze how promotional discourse is interdiscursively incorporated in the discourse by referring to the broader sociopolitical context.Findings have indicated five main moves:giving an overview,stressing historical status,displaying strengths,pledging political and ideological allegiance,and communicating goals and visions.Move 3,displaying strengths,has the greatest amount of information and can be further divided into six sub-moves which presents information on campus facilities,faculty team,talent cultivation,disciplinary fields construction,academic research,and international exchange.The main linguistic and rhetorical strategies used in these moves are analyzed and discussed.
基金This study is supported by the following projects:the Chinese Ministry of Education(MOE)Humanities and Social Science Research Funding(18YJC740042)the Research Project of Guangdong Planning Office of Philosophy and Social Science(GD16CWW05)+1 种基金the MOE Key Research Project of Humanities and Social Science(16JJD740006)conducted by the Center for Linguistics and Applied Linguistics(CLAL),Guangdong University of Foreign Studies(GDUFS)supported by both the Department of Education of Guangdong Province and the Center for Linguistics and Applied Linguistics at Guangdong University of Foreign Studies,P.R.China for the frontier research and its theoretical innovations in interpersonal pragmatics(“人际语用学前沿研究与理论创新”项目)(2018WZDXM006).
文摘Discourse markers(DMs) are characterized by multifunctionality in different contexts.This study addressed the use of the Chinese DM,na(那),as a solution to topical divergence,during clinical interactions with right-hemisphere-damaged(RHD) patients.Drawing on data collected from clinical interviews between psychotherapists and RHD patients,this study examined the functions of na in response to RHD topical divergence,focusing on the topic and attitudinal aspects.It was found that na was mainly employed by psychotherapists to mark a reproffer of interview topics(i.e.,an attempt to return to earlier topics),and a display of disalignment and disaffiliation with RHD topical divergence.These functions of na reflect the psychotherapists’ attempts to overcome communicative problems arising from RHD topical divergence,so as to ensure the achievement of the communicative goal.Thus,na can be interpreted as a compensatory strategy for dealing with RHD topical divergence on an interpersonal level.These findings not only expand our knowledge about the function spectrum of na,but also offer insights for RHD patients’ interlocutors to enhance conversational communication with RHD patients via the compensatory strategy.
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Traditional Chinese medicine (TCM) is gaining popularity in the treatment of psychiatric disorders that can be described and treated from either an Eastern or a Western perspective. In Eastern medicine, the disorders are described according to five diagnostic methods that are used in TCM: inspection, auscultation, olfaction, inquiry, and palpation, including tongue and pulse diagnosis.
基金supported by a grant from Ministry of Education,Taiwan,China under the Aiming for the Top University Plan at Taiwan Normal University,China
文摘Embodied semantics theory asserts that the meaning of action-related words is neurally represented through networks that overlap with or are identical to networks involved in sensory-motor processing. While some studies supporting this theory have focused on Chinese characters, less attention has been paid to their semantic radicals. Indeed, there is still disagreement about whether these radicals are processed independently. The present study investigated whether radicals are processed separately and, if so, whether this processing occurs in sensory-motor regions. Materials consisted of 72 high-frequency Chinese characters, with 18 in each of four categories： hand-action verbs with and without hand-radicals, and verbs not related to hand actions, with and without hand-radicals. Twenty-eight participants underwent functional MRI scans while reading the characters. Compared to characters without hand-radicals, reading characters with hand-radicals activated the right medial frontal gyrus. Verbs involving hand-action activated the left inferior parietal lobule, possibly reflecting integration of information in the radical with the semantic meaning of the verb. The findings may be consistent with embodied semantics theory and suggest that neural representation of radicals is indispensable in processing Chinese characters.
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.
文摘OBJECTIVE: To investigate the effectiveness of acupuncture in the treatment of chronic schizophrenia and co-morbid sleep disorders.METHODS: A 42-year-old German male outpatient,suffering from long-term schizophrenia and sleep disorders, entered the study. Acupuncture was used as a non-pharmacological intervention. In addition to his ongoing Western Medicine(pharmacological) treatment, the patient received 12 weekly(non-standardized) acupuncture treatments in the clinic. The Traditional Chinese Medicine(TCM) diagnosis, the psychological assessment and the actiwatch data were compared before and after the acupuncture treatment.RESULTS: The TCM diagnosis revealed a Liver Fire pattern before the acupuncture treatment, which was still present, although to a lesser degree, after the treatment. The psychological assessment revealed no change in the positive symptoms, but a small decrease in the negative symptoms and the general psychopathology of the patient. This was further illustrated by the small decrease in the number of depressive symptoms. The subjective sleep disorders improved markedly after acupuncture treatment, but the daytime sleepiness did not. The actiwatch results showed that after acupuncture treatment, the patient was moving less during sleep, but no significant results were found for the other sleep parameters.CONCLUSION: Acupuncture was found to be an effective non-pharmacological add-on method for treating subjective sleep disorders, and, to a lesser degree, objective sleep disorders and the negative symptoms of chronic schizophrenia. Future larger clinical trials with follow-up measurements are needed in order to replicate the present preliminary beneficial acupuncture findings and in order to determine whether the observed effects can be sustained.
文摘For the past several decades, it has been clear that the ability to write texts for academic and professional purposes （＂academic writing＂ in this article） is key to the success of adults in U.S. society--in school, in university courses, on tests that they need to take to progress through learning and into work, and in the workforce. Academic writing has specific features and involves approaches that are different from much of the writing that is done with adult learners, particularly those learning English as an additional language （e.g., described in reviews by Hinkel, 2015; Leki, Cumming, ＆ Silva, 2008; and a survey by Rosenfeld, Courtney, ＆ Fowles, 2004）. However, a recent survey of adult educators, conducted by the authors, found that academic writing has not been a focus in many adult education programs, and teachers receive limited professional development in this area and instructional support in implementing it. This article describes the importance of academic writing at all levels of adult education, the key features of academic writing, and the current state of writing instruction in adult education programs. It then describes the motivation for, design, and outcomes of a survey of and interviews with adult educators across the country on their preparation for and instructional practices with academic writing （conducted in 2014 and 2015）. Finally, it describes approaches that can be used in adult education programs to meet the writing proficiency needs of students at all levels and next steps that the adult education field might take.
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.
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 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.
