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Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval
The semantic matching capabilities of neural information retrieval can ameliorate synonymy and polysemy problems of symbolic approaches.
Interpretable Word Embedding Contextualization
In this paper, we propose a method of calibrating a word embedding, so that the semantic it conveys becomes more relevant to the context.
Elucidating Conceptual Properties from Word Embeddings
In this paper, we introduce a method of identifying the components (i. e. dimensions) of word embeddings that strongly signifies properties of a word.
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