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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 1201–1210 of 4002 papers

TitleStatusHype
Decoding Brain Activity Associated with Literal and Metaphoric Sentence Comprehension Using Distributional Semantic Models—0
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings—0
Deceptive Opinion Spam Detection Using Neural Network—0
Metaphor Interpretation Using Word Embeddings—0
Debiasing Word Embeddings Improves Multimodal Machine Translation—0
Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings—0
Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention—0
Automatic Labeling of Problem-Solving Dialogues for Computational Microgenetic Learning Analytics—0
Automatic Generation of Multiple-Choice Questions—0
Debiasing Embeddings for Reduced Gender Bias in Text Classification—0
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