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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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TitleStatusHype
Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation—0
Computational Detection of Intertextual Parallels in Biblical Hebrew: A Benchmark Study Using Transformer-Based Language Models—0
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
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