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Semantic Structure and Interpretability of Word Embeddings
TLDR
Dense word embeddings are substantially successful in capturing semantic relations among words, so a meaningful semantic structure must be present in the respective vector spaces. Expand
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Profile‐encoding reconstruction for multiple‐acquisition balanced steady‐state free precession imaging
The scan‐efficiency in multiple‐acquisition balanced steady‐state free precession imaging can be maintained by accelerating and reconstructing each phase‐cycled acquisition individually, but thisExpand
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Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI
TLDR
A statistically segregated sampling method is proposed for multiple-acquisition MRI. Expand
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Imparting Interpretability to Word Embeddings while Preserving Semantic Structure.
TLDR
We introduce an additive modification to the objective function of the embedding learning algorithm that encourages word embedding vectors that are semantically related to a predefined concept to take larger values along a specified dimension, while leaving the original semantic learning mechanism mostly unaffected. Expand
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Imparting Interpretability to Word Embeddings
TLDR
We introduce an additive modification to the objective function of the embedding learning algorithm that encourages word embedding vectors of words that are semantically related a predefined concept to take larger values along a specified dimension, while leaving the original semantic learning mechanism mostly unaffected. Expand
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Measuring cross-lingual semantic similarity across European languages
TLDR
This paper studies cross-lingual semantic similarity (CLSS) between five European languages (i.e. English, French, German, Spanish and Italian) via unsupervised word embeddings from a cross- lingual lexicon. Expand
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Generating Semantic Similarity Atlas for Natural Languages
TLDR
We leverage a recently proposed word embedding based method to generate a language similarity atlas for 76 different languages around the world. Expand
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Semantic similarity between Turkish and European languages using word embeddings
TLDR
In this study, semantic similarity between Turkish (two different corpora) and five basic European languages is calculated using word embeddings over a fixed vocabulary, obtained results are verified using statistical testing. Expand
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Interpretability analysis for Turkish word embeddings
TLDR
A semantic category dataset (ANKAT) that contains more than 4000 Turkish words grouped under 62 different categories is composed to quantitatively evaluate the interpretability of the word embeddings. Expand
  • 2
Measuring and improving interpretability of word embeddings using lexical resources
TLDR
We propose a statistical method to uncover the underlying latent semantic structure in the dense word embeddings. Expand
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