【摘 要】
：
Automatically building semantic resources is essential to low resource-languages like Uyghur.However,Uyghur suffers from a lack of publicly available evaluation dataset for automatically building sema
【机 构】
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Department of Computer Science and Technology Institute for Artificial Intelligence State Key Lab on
【出 处】
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第十八届中国计算语言学大会暨中国中文信息学会2019学术年会
论文部分内容阅读
Automatically building semantic resources is essential to low resource-languages like Uyghur.However,Uyghur suffers from a lack of publicly available evaluation dataset for automatically building semantic resources like WordNet.To cope with this problem,first,we build the largest Uyghur-English and English-Uyghur dictionaries by exploiting many possible online and offline resources.Then by using Princeton WordNet(PWN)3.0 and Contemporary Uyghur Detailed Dictionary(CUDD),we construct an English-Uyghur WordNet evaluation dataset which is publicly available(https://github.com/kaharjan/uywordnet).In this dataset,more than 73,000 English synsets are mapped Uyghur automatically,in which over 20,000 are annotated manually.And the corresponding Uyghur words include definition and examples in Uyghur language context.We also propose a Synset Mapping based on Word Embeddings(SMWE)method.The experimental results on the dataset are promising.
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