The analysis of near-synonyms has always been a difficult point in English learning.There have been some relevant studies on the analysis of near-synonyms at home and abroad,but the examples are still insufficient.Bas...The analysis of near-synonyms has always been a difficult point in English learning.There have been some relevant studies on the analysis of near-synonyms at home and abroad,but the examples are still insufficient.Based on the Corpus of Contemporary American English(COCA),this paper takes“absolutely”and“utterly”as an example,and makes a comparative analysis from the five aspects of register,collocation,collocation,semantic preference and semantic prosody,aiming to make full use of the corpus in the analysis of English near-synonyms,providing a useful supplement for second language teaching and English learning.The results show that“absolutely”is more common in spoken language while utterly is more formal;regarding the collocation,both words are more associated with adjectives and verbs;in terms of colligation patterns,“absolutely”have six kinds of grammatical patterns while there are only four grammatical patterns of“utterly”;as for semantic preference,the semantic preference of“utterly”has a wider range than that of“absolutely”;for semantic prosody,“absolutely”have more neutral semantic prosody whereas“utterly”have stronger negative semantic prosody.This case study not only provides enlightenment for English learning and teaching,but also further enriches the corpus linguistics-related research.展开更多
English near-synonyms pose a great challenge for English learners and they often feel confused about them.This paper draws on the research findings of the distinction of synonymous pair:forbid and prohibit based on CO...English near-synonyms pose a great challenge for English learners and they often feel confused about them.This paper draws on the research findings of the distinction of synonymous pair:forbid and prohibit based on COCA(Corpus of Contemporary American English)corpus online.The result shows that for collocation,forbid may be used to collocate with the infinitive(to do)oring participle,while prohibit often collocates with the preposition from.After register analysis,it is found that prohibit is more for⁃mal than forbid.As for semantic prosody,forbid is associated with neutral collocates,whereas prohibit appears to suggest a more negative semantic prosody.Detailed comparison and contrast about the two verbs can thus help English learners grasp the differenc⁃es of English synonyms accurately.展开更多
Using Corpus of Contemporary American English as the source data,this paper carries out a corpus-based behavioral profile study to investigate four near-synonymous adjectives(serious,severe,grave,and grievous),focusin...Using Corpus of Contemporary American English as the source data,this paper carries out a corpus-based behavioral profile study to investigate four near-synonymous adjectives(serious,severe,grave,and grievous),focusing on their register and the types of nouns they each modify.Although sharing core meaning,these adjectives exhibit variations in formality levels and usage patterns.The identification of fine-grained usage differences complements the current inadequacies in describing these adjectives.Furthermore,the study reaffirms the effectiveness of the corpus-based behavioral profile approach in examining synonym differences.展开更多
The purpose of this paper is to construct a dataset of Mongolian near-synonymous compound qualitative adjectives to improve semantic analysis and develop semantic networks in the Mongolian language. By analyzing the c...The purpose of this paper is to construct a dataset of Mongolian near-synonymous compound qualitative adjectives to improve semantic analysis and develop semantic networks in the Mongolian language. By analyzing the characteristics of Mongolian compound adjectives, this paper examines the current challenge in the study of synonyms. In this paper, we use three methods based on dictionaries, word embedding, and pre-trained language models and successfully construct 76 near-sense sets containing 450 conjunctive adjectives. The experiments demonstrate that the dictionary-based method is more accurate but needs better recall. The word embedding-based method can discover new near-synonyms but is limited by data quality. The pre-trained language model method underperforms when dealing with complex contexts. Future work will expand dictionary resources, optimize matching rules, improve data quality, and explore alternative models to build a more comprehensive Mongolian near-synonym resource.展开更多
文摘The analysis of near-synonyms has always been a difficult point in English learning.There have been some relevant studies on the analysis of near-synonyms at home and abroad,but the examples are still insufficient.Based on the Corpus of Contemporary American English(COCA),this paper takes“absolutely”and“utterly”as an example,and makes a comparative analysis from the five aspects of register,collocation,collocation,semantic preference and semantic prosody,aiming to make full use of the corpus in the analysis of English near-synonyms,providing a useful supplement for second language teaching and English learning.The results show that“absolutely”is more common in spoken language while utterly is more formal;regarding the collocation,both words are more associated with adjectives and verbs;in terms of colligation patterns,“absolutely”have six kinds of grammatical patterns while there are only four grammatical patterns of“utterly”;as for semantic preference,the semantic preference of“utterly”has a wider range than that of“absolutely”;for semantic prosody,“absolutely”have more neutral semantic prosody whereas“utterly”have stronger negative semantic prosody.This case study not only provides enlightenment for English learning and teaching,but also further enriches the corpus linguistics-related research.
文摘English near-synonyms pose a great challenge for English learners and they often feel confused about them.This paper draws on the research findings of the distinction of synonymous pair:forbid and prohibit based on COCA(Corpus of Contemporary American English)corpus online.The result shows that for collocation,forbid may be used to collocate with the infinitive(to do)oring participle,while prohibit often collocates with the preposition from.After register analysis,it is found that prohibit is more for⁃mal than forbid.As for semantic prosody,forbid is associated with neutral collocates,whereas prohibit appears to suggest a more negative semantic prosody.Detailed comparison and contrast about the two verbs can thus help English learners grasp the differenc⁃es of English synonyms accurately.
文摘Using Corpus of Contemporary American English as the source data,this paper carries out a corpus-based behavioral profile study to investigate four near-synonymous adjectives(serious,severe,grave,and grievous),focusing on their register and the types of nouns they each modify.Although sharing core meaning,these adjectives exhibit variations in formality levels and usage patterns.The identification of fine-grained usage differences complements the current inadequacies in describing these adjectives.Furthermore,the study reaffirms the effectiveness of the corpus-based behavioral profile approach in examining synonym differences.
基金supported by the Doctoral Discipline Innovation Project of the Inner Mongolia Autonomous Region(No.B20231051Z).
文摘The purpose of this paper is to construct a dataset of Mongolian near-synonymous compound qualitative adjectives to improve semantic analysis and develop semantic networks in the Mongolian language. By analyzing the characteristics of Mongolian compound adjectives, this paper examines the current challenge in the study of synonyms. In this paper, we use three methods based on dictionaries, word embedding, and pre-trained language models and successfully construct 76 near-sense sets containing 450 conjunctive adjectives. The experiments demonstrate that the dictionary-based method is more accurate but needs better recall. The word embedding-based method can discover new near-synonyms but is limited by data quality. The pre-trained language model method underperforms when dealing with complex contexts. Future work will expand dictionary resources, optimize matching rules, improve data quality, and explore alternative models to build a more comprehensive Mongolian near-synonym resource.
