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 1861–1870 of 4002 papers

TitleStatusHype
Hierarchical Meta-Embeddings for Code-Switching Named Entity RecognitionCode0
Do We Need Neural Models to Explain Human Judgments of Acceptability?—0
Decision-Directed Data DecompositionCode0
Multi Sense Embeddings from Topic Models—0
Cross-Lingual BERT Transformation for Zero-Shot Dependency ParsingCode0
Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings—0
A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector SpacesCode0
Retrofitting Contextualized Word Embeddings with Paraphrases—0
Lost in Evaluation: Misleading Benchmarks for Bilingual Dictionary InductionCode0
A Robust Hybrid Approach for Textual Document ClassificationCode0
Show:102550
← PrevPage 187 of 401Next →

No leaderboard results yet.