Lexical analysis is a fundamental task in natural language processing,which involves several subtasks,such as word segmentation(WS),part-of-speech(POS)tagging,and named entity recognition(NER).Recent works have shown ...Lexical analysis is a fundamental task in natural language processing,which involves several subtasks,such as word segmentation(WS),part-of-speech(POS)tagging,and named entity recognition(NER).Recent works have shown that taking advantage of relatedness between these subtasks can be beneficial.This paper proposes a unified neural framework to address these subtasks simultaneously.Apart from the sequence tagging paradigm,the proposed method tackles the multitask lexical analysis via two-stage sequence span classification.Firstly,the model detects the word and named entity boundaries by multilabel classification over character spans in a sentence.Then,the authors assign POS labels and entity labels for words and named entities by multi-class classification,respectively.Furthermore,a Gated Task Transformation(GTT)is proposed to encourage the model to share valuable features between tasks.The performance of the proposed model was evaluated on Chinese and Thai public datasets,demonstrating state-of-the-art results.展开更多
Currently,in STEM environments,female employees are often recognized as minorities due to their positioning or occupancy rate,which may lead to experiences of“imposter syndrome”.This study applies frameworks of mixe...Currently,in STEM environments,female employees are often recognized as minorities due to their positioning or occupancy rate,which may lead to experiences of“imposter syndrome”.This study applies frameworks of mixed-gender discourse,such as limited involvement in activity as an agent,markedness,and gender-differentiated roles,to clarify how women in STEM position themselves or are positioned by the society.Using corpus linguistics and content analysis,it is clarified that female researchers are usually linguistically marked or tend to distinguish themselves as non-experts.Thus,their portrayal within a misogynistic society may considerably interact with how female researchers represent themselves.展开更多
I.IntroductionThe study of the.lexical approach focuses on the understanding ofa lexical-grammatical unit,which was called lexical phrase by Nattinger and Decarrieo and was called chunks by Michel Lewis.It is a multi-...I.IntroductionThe study of the.lexical approach focuses on the understanding ofa lexical-grammatical unit,which was called lexical phrase by Nattinger and Decarrieo and was called chunks by Michel Lewis.It is a multi-word unit of varying lengths,which has a fixed orrelatively fixed structure and expresses a certain meaning.It is prefabri-cated and frequently used.As a language teacher I think chunks arevery useful in language teaching and the lexical approach is a way of improving my teaching.They make sense in the classroom as they展开更多
This paper attempts at an in-depth exploration of lexical mismatching, a pattern of vocabulary errors. In this paper, errors per se were collected from 107 writing samples of Chinese college students, and then investi...This paper attempts at an in-depth exploration of lexical mismatching, a pattern of vocabulary errors. In this paper, errors per se were collected from 107 writing samples of Chinese college students, and then investigated from a variety of linguistic perspectives. Traditional theoretical approaches, i.e. CA and EA, are incorporated in the source analysis for a more comprehensive interpretation. Hopefully, the findings of the study will shed lights on lexical instruction and acquisition in the field of SLA.展开更多
Even though Turnitin generates AI(artificial intelligence)writing detection reports,these AI reports shall not be used for punitive purposes as Turnitin AI reports accuracy is way below the 98%claimed by Turnitin,as r...Even though Turnitin generates AI(artificial intelligence)writing detection reports,these AI reports shall not be used for punitive purposes as Turnitin AI reports accuracy is way below the 98%claimed by Turnitin,as revealed in this study.To assist professors,teachers,and content evaluation stakeholders in their strive to identify AI-generated material,this study examines the stylistic features of case study,business correspondence,and academic writing ChatGPT-4-generated responses by exploring sentence length,paragraph structure,word choice,mood,tense,voice,pronouns,keywords density,lexical density,lexical diversity,and reading ease.The study revealed that ChatGPT-4 case study-generated responses are produced in paragraphs of 2 to 3 sentences of 16 to 18 words each.The sentences are mainly formed in the imperative mood.The use of the second-person pronoun“you”and the second-person possessive determiner“your”is prevalent.Keywords and lexical density are relatively low,lexical diversity is average,and the reading ease is relatively high.The study also found that ChatGPT-4 business correspondence responses are generated in paragraphs of 2 to 3 sentences of 16 to 20 words each.The sentences are mainly generated in declarative mood thru simple present tense in active voice using third-person singular pronouns.Technical words and abbreviations are used without outlining what they stand for.The keywords density,lexical density,and lexical diversity are high,and the reading ease is low.The study also revealed that ChatGPT-4 academic writing-generated responses are provided in paragraphs of 3 to 4 sentences of 16 to 19 words each.The sentences are mainly generated in declarative mood using active voice,agentless passive in times,with diverse present tenses.Keywords and lexical densities are high,and the lexical diversity is low,which makes the reading ease average difficulty,except for the undefined abbreviations.Noticeably,ChatGPT-4 supports the transgender movement by intentionally using the third-person plural pronoun“they”to refer to a singular.展开更多
基金supported by National Natural Science Foundation of China(Grant No.62266028,62266027,U21B2027,and U24A20334)Major Science and Technology Programs in Yunnan Province(Grant No.202302AD080003,202402AG050007,and 202303AP140008)+1 种基金Yunnan Province Basic Research Program(Grant No.202301AS070047,202301AT070471,and 202401BC070021)Kunming University of Science and Technology's"Double First-rate"construction joint project(Grant No.202201BE070001-021).
文摘Lexical analysis is a fundamental task in natural language processing,which involves several subtasks,such as word segmentation(WS),part-of-speech(POS)tagging,and named entity recognition(NER).Recent works have shown that taking advantage of relatedness between these subtasks can be beneficial.This paper proposes a unified neural framework to address these subtasks simultaneously.Apart from the sequence tagging paradigm,the proposed method tackles the multitask lexical analysis via two-stage sequence span classification.Firstly,the model detects the word and named entity boundaries by multilabel classification over character spans in a sentence.Then,the authors assign POS labels and entity labels for words and named entities by multi-class classification,respectively.Furthermore,a Gated Task Transformation(GTT)is proposed to encourage the model to share valuable features between tasks.The performance of the proposed model was evaluated on Chinese and Thai public datasets,demonstrating state-of-the-art results.
文摘Currently,in STEM environments,female employees are often recognized as minorities due to their positioning or occupancy rate,which may lead to experiences of“imposter syndrome”.This study applies frameworks of mixed-gender discourse,such as limited involvement in activity as an agent,markedness,and gender-differentiated roles,to clarify how women in STEM position themselves or are positioned by the society.Using corpus linguistics and content analysis,it is clarified that female researchers are usually linguistically marked or tend to distinguish themselves as non-experts.Thus,their portrayal within a misogynistic society may considerably interact with how female researchers represent themselves.
文摘I.IntroductionThe study of the.lexical approach focuses on the understanding ofa lexical-grammatical unit,which was called lexical phrase by Nattinger and Decarrieo and was called chunks by Michel Lewis.It is a multi-word unit of varying lengths,which has a fixed orrelatively fixed structure and expresses a certain meaning.It is prefabri-cated and frequently used.As a language teacher I think chunks arevery useful in language teaching and the lexical approach is a way of improving my teaching.They make sense in the classroom as they
文摘This paper attempts at an in-depth exploration of lexical mismatching, a pattern of vocabulary errors. In this paper, errors per se were collected from 107 writing samples of Chinese college students, and then investigated from a variety of linguistic perspectives. Traditional theoretical approaches, i.e. CA and EA, are incorporated in the source analysis for a more comprehensive interpretation. Hopefully, the findings of the study will shed lights on lexical instruction and acquisition in the field of SLA.
文摘Even though Turnitin generates AI(artificial intelligence)writing detection reports,these AI reports shall not be used for punitive purposes as Turnitin AI reports accuracy is way below the 98%claimed by Turnitin,as revealed in this study.To assist professors,teachers,and content evaluation stakeholders in their strive to identify AI-generated material,this study examines the stylistic features of case study,business correspondence,and academic writing ChatGPT-4-generated responses by exploring sentence length,paragraph structure,word choice,mood,tense,voice,pronouns,keywords density,lexical density,lexical diversity,and reading ease.The study revealed that ChatGPT-4 case study-generated responses are produced in paragraphs of 2 to 3 sentences of 16 to 18 words each.The sentences are mainly formed in the imperative mood.The use of the second-person pronoun“you”and the second-person possessive determiner“your”is prevalent.Keywords and lexical density are relatively low,lexical diversity is average,and the reading ease is relatively high.The study also found that ChatGPT-4 business correspondence responses are generated in paragraphs of 2 to 3 sentences of 16 to 20 words each.The sentences are mainly generated in declarative mood thru simple present tense in active voice using third-person singular pronouns.Technical words and abbreviations are used without outlining what they stand for.The keywords density,lexical density,and lexical diversity are high,and the reading ease is low.The study also revealed that ChatGPT-4 academic writing-generated responses are provided in paragraphs of 3 to 4 sentences of 16 to 19 words each.The sentences are mainly generated in declarative mood using active voice,agentless passive in times,with diverse present tenses.Keywords and lexical densities are high,and the lexical diversity is low,which makes the reading ease average difficulty,except for the undefined abbreviations.Noticeably,ChatGPT-4 supports the transgender movement by intentionally using the third-person plural pronoun“they”to refer to a singular.
