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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 591–600 of 4002 papers

TitleStatusHype
Isomorphic Cross-lingual Embeddings for Low-Resource Languages—0
Probabilistic Embeddings with Laplacian Graph Priors—0
Gender and Racial Stereotype Detection in Legal Opinion Word Embeddings—0
Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset—0
Building Robust Spoken Language Understanding by Cross Attention between Phoneme Sequence and ASR Hypothesis—0
Compression of Generative Pre-trained Language Models via Quantization—0
VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot LearningCode1
From meaning to perception -- exploring the space between word and odor perception embeddings—0
Improving Word Translation via Two-Stage Contrastive LearningCode1
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little CostCode1
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