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 2726–2750 of 4002 papers

TitleStatusHype
Learning to Respond to Mixed-code Queries using Bilingual Word Embeddings—0
Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks—0
Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs—0
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy—0
Learning Unsupervised Word Translations Without Adversaries—0
Learning User Embeddings from Emails—0
Learning word embeddings efficiently with noise-contrastive estimation—0
Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets—0
Learning Word Embeddings for Hyponymy with Entailment-Based Distributional Semantics—0
Learning Word Embeddings for Low-Resource Languages by PU Learning—0
Learning Word Embeddings from Intrinsic and Extrinsic Views—0
Learning Word Embeddings from Speech—0
Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects—0
Learning Word Embeddings without Context Vectors—0
Learning Word Meta-Embeddings—0
Learning Word Representations from Scarce and Noisy Data with Embedding Subspaces—0
Learning Word Representations with Regularization from Prior Knowledge—0
Learning Word Sense Embeddings from Word Sense Definitions—0
Learn Interpretable Word Embeddings Efficiently with von Mises-Fisher Distribution—0
Learnt Contrastive Concept Embeddings for Sign Recognition—0
Legal Document Classification: An Application to Law Area Prediction of Petitions to Public Prosecution Service—0
Legal-ES: A Set of Large Scale Resources for Spanish Legal Text Processing—0
Lego: Learning to Disentangle and Invert Personalized Concepts Beyond Object Appearance in Text-to-Image Diffusion Models—0
Lessons in Reproducibility: Insights from NLP Studies in Materials Science—0
Lessons Learned from Applying off-the-shelf BERT: There is no Silver Bullet—0
Show:102550
← PrevPage 110 of 161Next →

No leaderboard results yet.