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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 2101–2125 of 4002 papers

TitleStatusHype
Reverse Transfer Learning: Can Word Embeddings Trained for Different NLP Tasks Improve Neural Language Models?—0
Revisiting Additive Compositionality: AND, OR and NOT Operations with Word Embeddings—0
Revisiting Additive Compositionality: AND, OR, and NOT Operations with Word Embeddings—0
Revisiting Embedding Features for Simple Semi-supervised Learning—0
Revisiting Representation Degeneration Problem in Language Modeling—0
Revisiting Statistical Laws of Semantic Shift in Romance Cognates—0
Revisiting Supertagging and Parsing: How to Use Supertags in Transition-Based Parsing—0
Revisiting the Context Window for Cross-lingual Word Embeddings—0
Revisiting Word Embeddings in the LLM Era—0
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems—0
Right-truncatable Neural Word Embeddings—0
Risk Bounds for Transferring Representations With and Without Fine-Tuning—0
Robust and Consistent Estimation of Word Embedding for Bangla Language by fine-tuning Word2Vec Model—0
Robust Backed-off Estimation of Out-of-Vocabulary Embeddings—0
Robust Concept Erasure Using Task Vectors—0
Robust Word Vectors: Context-Informed Embeddings for Noisy Texts—0
Roleo: Visualising Thematic Fit Spaces on the Web—0
Romanian micro-blogging named entity recognition including health-related entities—0
Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings—0
Rotate King to get Queen: Word Relationships as Orthogonal Transformations in Embedding Space—0
Rotations and Interpretability of Word Embeddings: the Case of the Russian Language—0
RPD: A Distance Function Between Word Embeddings—0
R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models—0
RS\_GV at SemEval-2021 Task 1: Sense Relative Lexical Complexity Prediction—0
RUFINO at SemEval-2017 Task 2: Cross-lingual lexical similarity by extending PMI and word embeddings systems with a Swadesh's-like list—0
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