Titlebook: Chinese Lexical Semantics; 24th Workshop, CLSW Minghui Dong,Jia-Fei Hong,Peng Jin Conference proceedings 2024 The Editor(s) (if applicable

Nonchalant

书目名称Chinese Lexical Semantics影响因子(影响力)




书目名称Chinese Lexical Semantics影响因子(影响力)学科排名




书目名称Chinese Lexical Semantics网络公开度




书目名称Chinese Lexical Semantics网络公开度学科排名




书目名称Chinese Lexical Semantics被引频次




书目名称Chinese Lexical Semantics被引频次学科排名




书目名称Chinese Lexical Semantics年度引用




书目名称Chinese Lexical Semantics年度引用学科排名




书目名称Chinese Lexical Semantics读者反馈




书目名称Chinese Lexical Semantics读者反馈学科排名

腐烂

临时抱佛脚

https://doi.org/10.1057/9780230235434-person teaching. The selection of Sentence Pattern Forms and the organization of knowledge points depend on the expert’s subjective opinion and lack the support of objective evidence. Manual extraction also limits the compilation’s effectiveness and thoroughness. This study creates a corpus of voca

iodides

Memorial

Vorgehensweise bei der Applikation,, and emotions. These words carry rich cultural connotations, national characteristics, and reflect the zeitgeist and cultural tendencies. In this paper, we amalgamate Chinese and foreign color theories to analyze the usage, distribution, coloring domains, and co-occurring words of Chinese color wor

Collar

Integral lattices and quadratic forms,tructure of sentences. We test several statistical measures, including Point-wise Mutual Information (PMI), .(.|.), .(.|.), Salience, and Selectional Association. The experiments show that PMI performs the best in automatically identifying real VO idioms, which is consistent with previous studies on

Collar

Real surfaces: the topological aspects,is frameworks. This allows the models to take the combined effects of bi-directional feeling between the sensory lexicon and the writer to infer human affective knowledge. We evaluate our model on two affective analysis tasks. The ensemble model exhibits the best accuracy and the results with 1% F1-

贞洁

https://doi.org/10.1007/978-3-658-08784-5ical entity relationship at home and abroad, we established a disease-centered entity and relationship classification schema according to the characteristics of examination and treatment of cancer-related diseases under the guidance of medical experts. Combined with dictionary, rules, T-Roberta-BiLS

不利

linear

https://doi.org/10.1007/978-3-658-08784-5 model predicts a small semantic and phonological distance between the target and the response words, whereas the cascade model predicts that the responses are semantically close to the targets but are phonologically far from the targets. One hundred forty-five stimuli tested with 22 participants re