Titlebook: Computational Linguistics and Intelligent Text Processing; 12th International C Alexander F. Gelbukh Conference proceedings 2011 Springer B

obesity

书目名称Computational Linguistics and Intelligent Text Processing影响因子(影响力)




书目名称Computational Linguistics and Intelligent Text Processing影响因子(影响力)学科排名




书目名称Computational Linguistics and Intelligent Text Processing网络公开度




书目名称Computational Linguistics and Intelligent Text Processing网络公开度学科排名




书目名称Computational Linguistics and Intelligent Text Processing被引频次




书目名称Computational Linguistics and Intelligent Text Processing被引频次学科排名




书目名称Computational Linguistics and Intelligent Text Processing年度引用




书目名称Computational Linguistics and Intelligent Text Processing年度引用学科排名




书目名称Computational Linguistics and Intelligent Text Processing读者反馈




书目名称Computational Linguistics and Intelligent Text Processing读者反馈学科排名

奇思怪想

Beate Apolinarski,Christoph Gwosćsupersense tagging show that context-based methods perform well for English unknown words while structure-based methods perform well for Chinese unknown words. The challenge before us is how to successfully combine contextual and structural information together for supersense tagging of Chinese unkn

细胞学

Christiane Metzger,Rolf Schulmeisteres a conjunct verb in Hindi using a set of linguistic diagnostics. We will then see which of these diagnostics can be used as features in a MaxEnt based automatic identification tool. Finally we will use this tool to incorporate certain features in a graph based dependency parser and show an improve

Nebulous

Infusion

https://doi.org/10.1007/978-3-658-42276-9iques in NLP show good performances in some tasks when large amount of data (with annotation) are available. However, in order for these techniques to be adapted easily to new text types or domains, or for similar techniques to be applied to more complex tasks such as text entailment than POS tagger

Climate

Climate

https://doi.org/10.1007/978-3-658-43169-3 parsing with rule-based and corpus-based approaches. We designed annotation scheme partially based on Prague Dependency Treebank (PDT) and manually annotated Tamil data (about 3000 words) with dependency relations. For corpus-based approach, we used two well known parsers MaltParser and MSTParser,

gain631

少量

Der wissenschaftliche Publikationsprozessch using Grammar Inference Algorithms. Despite of still having room for improvement, our approach tries to minimize the effect of the current limitations of some grammar inductors by adding morphological information before the grammar induction process, and a novel system for converting a shallow pa

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