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- W2891378076 abstract "Most models for learning word embeddings are trained based on the context information of words, more precisely first order co-occurrence relations. In this paper, a metric is designed to estimate second order co-occurrence relations based on context overlap. The estimated values are further used as the augmented data to enhance the learning of word embeddings by joint training with existing neural word embedding models. Experimental results show that better word vectors can be obtained for word similarity tasks and some downstream NLP tasks by the enhanced approach." @default.
- W2891378076 created "2018-09-27" @default.
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- W2891378076 date "2018-01-01" @default.
- W2891378076 modified "2023-10-12" @default.
- W2891378076 title "Quantifying Context Overlap for Training Word Embeddings" @default.
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- W2891378076 doi "https://doi.org/10.18653/v1/d18-1057" @default.
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