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- W3175605827 abstract "This article presents a systematic literature review on quantifying the proximity between independently trained monolingual word embedding spaces. A search was carried out in the broader context of inducing bilingual lexicons from cross-lingual word embeddings, especially for low-resource languages. The returned articles were then classified. Cross-lingual word embeddings have drawn the attention of researchers in the field of natural language processing (NLP). Although existing methods have yielded satisfactory results for resource-rich languages and languages related to them, some researchers have pointed out that the same is not true for low-resource and distant languages. In this paper, we report the research on methods proposed to provide better representation for low-resource and distant languages in the cross-lingual word embedding space." @default.
- W3175605827 created "2021-07-05" @default.
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- W3175605827 date "2021-07-01" @default.
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- W3175605827 title "Leveraging Vector Space Similarity for Learning Cross-Lingual Word Embeddings: A Systematic Review" @default.
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- W3175605827 doi "https://doi.org/10.3390/digital1030011" @default.
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