Titlebook: Chinese Language Resources; Data Collection, Lin Chu-Ren Huang,Shu-Kai Hsieh,Peng Jin Book 2023 Springer Nature Switzerland AG 2023 languag

风俗习惯

书目名称Chinese Language Resources影响因子(影响力)




书目名称Chinese Language Resources影响因子(影响力)学科排名




书目名称Chinese Language Resources网络公开度




书目名称Chinese Language Resources网络公开度学科排名




书目名称Chinese Language Resources被引频次




书目名称Chinese Language Resources被引频次学科排名




书目名称Chinese Language Resources年度引用




书目名称Chinese Language Resources年度引用学科排名




书目名称Chinese Language Resources读者反馈




书目名称Chinese Language Resources读者反馈学科排名

忍耐

改良

散布

Reaktive Sauerstoffspezies in der Medizing of Chinese texts. While this goal is within reach, it has yet to be attained even after more than 30 years of Chinese language processing research. Most recent achievements in Chinese adopt either stochastic or deep learning models that rely heavily on the availability of training data. As such, t

bifurcate

Pelago

Pelago

E. F. Elstner,W. Schütz,G. Voglll first describe the triune Chinese generalized function word usage knowledge base, which includes a usage dictionary, a usage rule base, a usage corpus, and defined function words such as adverbs, prepositions, conjunctions, auxiliaries, modal particles, and location words. With this constructed k

壕沟

stress-test

https://doi.org/10.1007/978-3-642-71583-9nding. The major extension features include the following: (a) word senses are defined by either primitives or any well-defined concepts and conceptual relations; (b) semantic relations are explicitly expressed; (c) uniform representation of content words, function words, and phrases; (d) semantic c

CRUDE

https://doi.org/10.1007/978-3-642-71583-9(POS) filtering, and suffix filtering. The model first utilized the external feature of contexts to find synonyms of unknown words by training the contexts with word embedding and then detected the semantic categories of these synonyms in ., a Chinese thesaurus. Internal features such as POS and suf