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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 251–260 of 4002 papers

TitleStatusHype
Including Semantic Information via Word Embeddings for Skeleton-based Action Recognition—0
Low-resource keyword spotting using contrastively trained transformer acoustic word embeddings—0
Characterizing Linguistic Shifts in Croatian News via Diachronic Word EmbeddingsCode0
Learning Obfuscations Of LLM Embedding Sequences: Stained Glass Transform—0
Recommender systems, stigmergy, and the tyranny of popularity—0
Static Word Embeddings for Sentence Semantic Representation—0
Cross-Domain Bilingual Lexicon Induction via Pretrained Language Models—0
On the Emergence of Linear Analogies in Word Embeddings—0
Bridging the Modality Gap: Enhancing Channel Prediction with Semantically Aligned LLMs and Knowledge Distillation—0
A Comparative Analysis of Static Word Embeddings for HungarianCode0
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