SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 11–20 of 4002 papers

TitleStatusHype
Bridging the Modality Gap: Enhancing Channel Prediction with Semantically Aligned LLMs and Knowledge Distillation—0
A Comparative Analysis of Static Word Embeddings for HungarianCode0
An Exploratory Analysis on the Explanatory Potential of Embedding-Based Measures of Semantic Transparency for Malay Word Recognition—0
Towards Smart Point-and-Shoot Photography—0
Word Embedding Techniques for Classification of Star Ratings—0
Word Embeddings Track Social Group Changes Across 70 Years in China—0
Geological Inference from Textual Data using Word EmbeddingsCode0
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections—0
myNER: Contextualized Burmese Named Entity Recognition with Bidirectional LSTM and fastText Embeddings via Joint Training with POS TaggingCode0
Investigating and Mitigating Stereotype-aware Unfairness in LLM-based Recommendations—0
Show:102550
← PrevPage 2 of 401Next →

No leaderboard results yet.