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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 1611–1620 of 4002 papers

TitleStatusHype
Improving Disfluency Detection by Self-Training a Self-Attentive Model—0
DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment CorpusCode1
Word Equations: Inherently Interpretable Sparse Word Embeddingsthrough Sparse Coding—0
Decomposing Word Embedding with the Capsule Network—0
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation—0
Information-Theoretic Probing for Linguistic StructureCode1
Geometry-aware Domain Adaptation for Unsupervised Alignment of Word Embeddings—0
Analyzing autoencoder-based acoustic word embeddings—0
4chan & 8chan embeddings—0
Understanding Linearity of Cross-Lingual Word Embedding MappingsCode1
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