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 1021–1030 of 4002 papers

TitleStatusHype
When Word Embeddings Become Endangered—0
Monolingual and Parallel Corpora for Kangri Low Resource LanguageCode0
SparseGAN: Sparse Generative Adversarial Network for Text Generation—0
SEMIE: SEMantically Infused Embeddings with Enhanced Interpretability for Domain-specific Small Corpus—0
Attention-based model for predicting question relatedness on Stack Overflow—0
Acoustic word embeddings for zero-resource languages using self-supervised contrastive learning and multilingual adaptationCode0
Do Word Embeddings Really Understand Loughran-McDonald's Polarities?—0
DeepHate: Hate Speech Detection via Multi-Faceted Text Representations—0
Cooperative Self-training of Machine Reading ComprehensionCode1
Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist NewspapersCode0
Show:102550
← PrevPage 103 of 401Next →

No leaderboard results yet.