malcontented 发表于 2025-3-21 16:17:00
书目名称Computational Processing of the Portuguese Language影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0232922<br><br> <br><br>书目名称Computational Processing of the Portuguese Language影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0232922<br><br> <br><br>书目名称Computational Processing of the Portuguese Language网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0232922<br><br> <br><br>书目名称Computational Processing of the Portuguese Language网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0232922<br><br> <br><br>书目名称Computational Processing of the Portuguese Language被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0232922<br><br> <br><br>书目名称Computational Processing of the Portuguese Language被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0232922<br><br> <br><br>书目名称Computational Processing of the Portuguese Language年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0232922<br><br> <br><br>书目名称Computational Processing of the Portuguese Language年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0232922<br><br> <br><br>书目名称Computational Processing of the Portuguese Language读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0232922<br><br> <br><br>书目名称Computational Processing of the Portuguese Language读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0232922<br><br> <br><br>绿州 发表于 2025-3-21 21:36:41
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Conference proceedings 2022in Fortaleza, Brazil, in March 2021...The 36 full papers presented together with 4 short papers were carefully reviewed and selected from 88 submissions. They are grouped in topical sections on speech processing; resources and evaluation; natural language processing applications; semantics; natural谷物 发表于 2025-3-23 04:51:06
0302-9743submissions. They are grouped in topical sections on speech processing; resources and evaluation; natural language processing applications; semantics; natural language processing tasks; and multilinguality..978-3-030-98304-8978-3-030-98305-5Series ISSN 0302-9743 Series E-ISSN 1611-3349诱拐 发表于 2025-3-23 07:17:57
Koordination von Zuliefernetzwerkennd an internally built NMT system trained over 30 h on 2,5M sentences. It was found that ambiguity, named entity & terminology and verb valency are the categories where MT systems struggle most. Negation, pronouns, subordination, verb tense/aspect/mood and false friends are the categories where MT systems perform best.