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
互联网上的攻击性言论严重扰乱了正常网络秩序,破坏了健康交流的网络环境。现有的检测技术更关注文本中的鲜明特征,难以发现更隐晦的攻击方式。针对上述问题,提出融合反讽机制的攻击性言论检测模型BSWD(Bidirectional Encoder Represent...互联网上的攻击性言论严重扰乱了正常网络秩序,破坏了健康交流的网络环境。现有的检测技术更关注文本中的鲜明特征,难以发现更隐晦的攻击方式。针对上述问题,提出融合反讽机制的攻击性言论检测模型BSWD(Bidirectional Encoder Representation from Transformers-based Sarcasm and Word Detection)。首先,提出基于反讽机制的模型Sarcasm-BERT,以检测言论中的语义冲突;其次,提出细粒度词汇攻击性特征提取模型WordsDetect,检测言论中的攻击性词汇;最后,融合两种模型得到BSWD。实验结果表明,与BERT(Bidirectional Encoder Representation from Transformers)、HateBERT模型相比,所提模型的准确率、精确率、召回率和F1分数指标大部分能提升2%,显著提高了检测性能,更能发现隐含的攻击性言论;同时,与SKS(Sentiment Knowledge Sharing)、BiCHAT(Bidirectional long shortterm memory with deep Convolution neural network and Hierarchical ATtention)模型相比,具有更强的泛化能力和鲁棒性。以上结果验证了BSWD检测隐晦攻击性言论的有效性。展开更多
By the brief introduction of Kate Chopin and her achievement, this paper elaborates the awakening of consciousness of the feminism of the protagonist in Kate Chopin's The Story of an Hour. In the short story, the ...By the brief introduction of Kate Chopin and her achievement, this paper elaborates the awakening of consciousness of the feminism of the protagonist in Kate Chopin's The Story of an Hour. In the short story, the author uses many literary elements to describe the characters, especially the irony and imagery. This thesis uses those rhetorical devices to vividly describe the characters and to criticize the inequality between men and women in the late 19th century.[1]展开更多
Mark Twain is a realistic writer in America.His humor,irony and together with colloquial speech have been remembered by readers,for which he became one of America's most beloved humorists and storytellers.This pap...Mark Twain is a realistic writer in America.His humor,irony and together with colloquial speech have been remembered by readers,for which he became one of America's most beloved humorists and storytellers.This paper focuses on one of Twain's short stories The Stolen White Elephant.Through the analysis of Twain's humor and irony shown from the two main characters in this specific story,this paper is to show the social reality reflected in The Stolen White Elephant.展开更多
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.
文摘互联网上的攻击性言论严重扰乱了正常网络秩序,破坏了健康交流的网络环境。现有的检测技术更关注文本中的鲜明特征,难以发现更隐晦的攻击方式。针对上述问题,提出融合反讽机制的攻击性言论检测模型BSWD(Bidirectional Encoder Representation from Transformers-based Sarcasm and Word Detection)。首先,提出基于反讽机制的模型Sarcasm-BERT,以检测言论中的语义冲突;其次,提出细粒度词汇攻击性特征提取模型WordsDetect,检测言论中的攻击性词汇;最后,融合两种模型得到BSWD。实验结果表明,与BERT(Bidirectional Encoder Representation from Transformers)、HateBERT模型相比,所提模型的准确率、精确率、召回率和F1分数指标大部分能提升2%,显著提高了检测性能,更能发现隐含的攻击性言论;同时,与SKS(Sentiment Knowledge Sharing)、BiCHAT(Bidirectional long shortterm memory with deep Convolution neural network and Hierarchical ATtention)模型相比,具有更强的泛化能力和鲁棒性。以上结果验证了BSWD检测隐晦攻击性言论的有效性。
文摘By the brief introduction of Kate Chopin and her achievement, this paper elaborates the awakening of consciousness of the feminism of the protagonist in Kate Chopin's The Story of an Hour. In the short story, the author uses many literary elements to describe the characters, especially the irony and imagery. This thesis uses those rhetorical devices to vividly describe the characters and to criticize the inequality between men and women in the late 19th century.[1]
文摘Mark Twain is a realistic writer in America.His humor,irony and together with colloquial speech have been remembered by readers,for which he became one of America's most beloved humorists and storytellers.This paper focuses on one of Twain's short stories The Stolen White Elephant.Through the analysis of Twain's humor and irony shown from the two main characters in this specific story,this paper is to show the social reality reflected in The Stolen White Elephant.
