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The Comparative Analysis of the Nouns Indicating a Person in Chinese and English Neologisms

2010· article· en· W1894744622 on OpenAlexvenueno aff
Ming Chen, Penkova Varvara

Bibliographic record

VenueCross-cultural communication · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicLexicography and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNeologismLinguisticsMeaning (existential)HumanitiesNounWord formationSemantic analysis (machine learning)SociologyPhilosophyComputer scienceArtificial intelligenceEpistemology

Abstract

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Neologisms are the words that appeared most recently in the process of the society development. They are not only a new phenomenon of the language itself, they also best reflect the changes in the society. With help of the semantic field theory, the author has carried out a comparative analysis of the meaning of nouns indicating a person in the two languages, has found out that the society background of the two languages, the lifestyles of the speakers, the focuses of attention have something in common, however, the differences are more numerous. Besides, through the structural analysis of the nouns indicating a person, we have analyzed the word-formation of Chinese and English words, the similarities and differences of the elements of compounds, the changes in the number of roots etc. Key words: neologisms; nouns indicating a person; semantic field; meaning; structure Resume: Les Neologismes sont les mots qui apparaissent le plus recemment dans le processus du developpement social. Ils ne sont pas seulement un phenomene nouveau de la langue meme, mais aussi les meilleurs reflets des changements dans la societe. Avec l'aide de la theorie des champs semantiques, l'auteur a effectue une analyse comparative de la signification des noms indiquants une personne dans les deux langues et il a decouvert que l’environnement social de ces deux langues, les modes de vie des locuteurs et les centres d’attention ont quelques choses en commun, toutefois, les differences sont plus nombreuses. En outre, par le biais de l'analyse structurelle des noms indiquants une personne, nous avons analyse la formation des mots en chinois et en anglais, les similitudes et les differences des elements de composes, les changements dans le nombre de racines etc Mots-cles: les neologismes, des noms indiquants une personne; les champs semantiques; le sens; la structure 摘 要:新詞語即社會發展過程中最新產生的詞語,它們不僅是語言本身的新現象,同時最能反映社會的發展變 化。本文運用語義場理論,對兩種語言中的指人名詞的意義進行了比較研究,發現兩種語言各自的社會背景、語言使用者的生活方式,關注的重點既有相同性但更多是差異性。另外通過對指人名詞的結構分析,研究了漢語和英語的構詞方式,複合詞成分的異同及詞根數量的變化等。 關鍵詞:新詞;指人名詞;語義場;意義;結構

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.320
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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