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Word Embeddings

Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers.

Techniques for learning word embeddings can include Word2Vec, GloVe, and other neural network-based approaches that train on an NLP task such as language modeling or document classification.

( Image credit: Dynamic Word Embedding for Evolving Semantic Discovery )

Papers

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Showing 2751–2760 of 4002 papers

TitleStatusHype
Word embedding and neural network on grammatical gender -- A case study of Swedish—0
Word Embedding and WordNet Based Metaphor Identification and Interpretation—0
Word Embedding-based Antonym Detection using Thesauri and Distributional Information—0
Word Embedding-Based Automatic MT Evaluation Metric using Word Position Information—0
Word-Embedding based Content Features for Automated Oral Proficiency Scoring—0
Word Embedding Calculus in Meaningful Ultradense Subspaces—0
Word Embedding Evaluation and Combination—0
Word Embedding Evaluation Datasets and Wikipedia Title Embedding for Chinese—0
Word Embedding Evaluation for Sinhala—0
Word Embedding Evaluation in Downstream Tasks and Semantic Analogies—0
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